Valence & Arousal: Analysis

Set-Up

Note, DecTools mascks ICC and SD from the psych package.

#Packages
library(tidyverse)       # data manipulation
library(psych)           # data descriptives
library(DescTools)       # Lin's Correspondance Coefficient
library(gt)              # table formatting
library(gtable)          # table formatting
library(gtsummary)       # table summaries
library(kableExtra)      # table formatting
library(knitr)           # html table formatting
library(irr)             # interrater reliabilities
library(stringr)         # work with strings
library(labelled)        # work with labels
library(lubridate)       # date formatting
library(viridis)         # color pallets 
library(plotly)          # 3D plots

#Set gt_theme for summary tables
theme_gtsummary_compact()

Import Files

Note on the OASIS file:

The OASIS study (Kurdi et al., 2017), human participants rated the images on a 1-7 likert scale. All subsequent machine and human ratings were done on a 1-9 likert scale.

The variables are denoted as follows:

  • “actual” OASIS vars (actual_valence & actual_arousal) were the participant rated OASIS images on a 1-7 scale.

  • “adjusted” OASIS values (adjusted_valence & adjusted_arousal) are the participant values adjusted to a 1-9 scale.

Kurdi, B., Lozano, S., & Banaji, M. R. (2017). Introducing the Open Affective Standardized Image Set (OASIS). Behavior Research Methods, 49(2), 457–470. https://doi.org/10.3758/s13428-016-0715-3

#Human Data
image_ratings_human_url <- "https://raw.githubusercontent.com/The-Change-Lab/affectivedynamics/main/data/image_ratings_human.csv"
image_ratings_human <- read.csv(file=url(image_ratings_human_url), header=T)

#All Ratings
image_ratings_all_url <- "https://raw.githubusercontent.com/The-Change-Lab/affectivedynamics/main/data/image_ratings_all.csv"
image_ratings_all <- read.csv(file=url(image_ratings_all_url), header=T)

#Day in the Life Deep Affect Modules (DAM)
image_ratings_DITL_url <- "https://raw.githubusercontent.com/The-Change-Lab/affectivedynamics/main/data/image_ratings_DITL.csv"
image_ratings_DITL <- read.csv(file=url(image_ratings_DITL_url), header=T)

Reshape & Subset Files

Resphaping and subsetting files for later plotting & analysis.

#Ratings by source
image_ratings_smartphone <- image_ratings_all %>%
  filter(source == "smartphone")

image_ratings_OASIS <- image_ratings_all %>%
  filter(source == "OASIS")

#Long form valence ratings
valence_long <- image_ratings_all %>%
  select(image, valence_human, valence_ml, source) %>%
  pivot_longer(cols = c("valence_human", "valence_ml"),
               names_to = "raiter", 
               values_to = "valence") %>%
  mutate(raiter = ifelse(raiter == "valence_human", "human", "machine"))

#Long form arousal ratings
arousal_long <- image_ratings_all %>%
  select(image, arousal_human, arousal_ml, source) %>%
  pivot_longer(cols = c("arousal_human", "arousal_ml"),
               names_to = "raiter", 
               values_to = "arousal") %>%
  mutate(raiter = ifelse(raiter == "arousal_human", "human", "machine"))

#Long form ratings
image_ratings_all_long <- merge(valence_long, arousal_long, by = c("image", "raiter"))
image_ratings_all_long <- image_ratings_all_long %>%
  rename(source = source.x) %>%
  select(image, raiter, source, valence, arousal)

image_ratings_all_extralong <- image_ratings_all %>%
  select(image, arousal_human, arousal_ml, valence_human, valence_ml, source) %>%
  pivot_longer(cols = c("valence_human", "valence_ml", "arousal_human", "arousal_ml"),
               names_to = "raiter", 
               values_to = "value") %>%
  mutate(measure = gsub("_.*", "", raiter),
         raiter = gsub(".*_", "", raiter))

#DITL Label Images
image_ratings_DITL <- image_ratings_DITL %>%
  arrange(date) %>%
  mutate(row_id=row_number())

#DITL Long
image_ratings_DITL_long <- image_ratings_DITL %>%
  pivot_longer(cols = c("valence", "arousal"),
               names_to = "rating")

Data Overview

Source

#Source
image_ratings_human %>%
  mutate(source = as_factor(source)) %>%
  select(source) %>%
  tbl_summary(
    label = list(source ~ "Source"),
    statistic = list(all_continuous() ~ "{mean} ({min}, {max})"),
    missing = "ifany") %>%
  modify_header(label ~ "") %>%
  as_gt() %>%
  tab_header("Valence & Arousal Survey Data")
Valence & Arousal Survey Data
N = 8321
Source
    student 381 (46%)
    prolific 451 (54%)
1 n (%)

Demographics

#Basic Demographics
image_ratings_human %>%
  mutate(gender = factor(gender, 
                         levels=c(1,2,3,4,5),
                         labels=c("Male", "Female", "Non-binary", "Other", 
                                  "Prefer not to answer")),
         race = factor(race,
                       levels=c(1,2,3,4,5,6,7),
                       labels=c("White", "Native American", "Asian", "Black", "Other", 
                                  "Prefer not to answer", "Multiracial")),
         hispanic = factor(hispanic,
                           levels=c(1,2,3),
                           labels=c("Hispanic or Latino/Latina",
                                    "Not Hispanic or Latino/Latina",
                                    "I prefer not to answer"))) %>%
  select(age, gender, race, hispanic) %>%
  tbl_summary(
    label = list(age ~"Age", gender ~ "Gender", race ~ "Race", hispanic ~ "Hispanic"),
    statistic = list(all_continuous() ~ "{mean} ({min}, {max})"),
    missing = "ifany") %>%
  modify_header(label ~ "") %>%
  as_gt() %>%
  tab_header("Valence & Arousal Survey Data")
Valence & Arousal Survey Data
N = 8321
Age 29 (18, 75)
Gender
    Male 374 (45%)
    Female 443 (53%)
    Non-binary 6 (0.7%)
    Other 5 (0.6%)
    Prefer not to answer 4 (0.5%)
Race
    White 478 (58%)
    Native American 8 (1.0%)
    Asian 163 (20%)
    Black 59 (7.1%)
    Other 53 (6.4%)
    Prefer not to answer 22 (2.6%)
    Multiracial 48 (5.8%)
    Unknown 1
Hispanic
    Hispanic or Latino/Latina 134 (16%)
    Not Hispanic or Latino/Latina 673 (81%)
    I prefer not to answer 25 (3.0%)
1 Mean (Range); n (%) 
#Expanded Demographics
image_ratings_human %>%
  mutate(education = factor(education,
                            levels=c(1,2,3,4,5,6,7,8),
                            labels=c("Less than high school", "Some college",
                                     "2-year college degree", "4-year college degree",
                                     "Master's degree", "Doctoral's degree",
                                     "Professional degree, e.g., JD/MD", 
                                     "High school/GED/Technical/vocational training)")),
         marital_status = factor(marital_status,
                                 levels=c(1,2,3,4),
                                 labels=c("Married, or living as married",
                                          "Divorced or separated",
                                          "Widowed",
                                          "Single/never married")),
         smartphone_hours = factor(smartphone_hours,
                                   levels=c(1,2,3,4,5),
                                   labels=c("0-1 hours", "1-3 hours",
                                            "3-5 hours", "5-7 hours",
                                            "More than 7 hours"))) %>%
  select(education, marital_status, smartphone_hours) %>%
  tbl_summary(
    label = list(education ~ "Education", 
                 marital_status ~ "Marital Status",
                 smartphone_hours ~ "Smartphone Hours"),
    statistic = list(all_continuous() ~ "{mean} ({min}, {max})"),
    missing = "ifany") %>%
  modify_header(label ~ "") %>%
  as_gt() %>%
  tab_header("Valence & Arousal Survey Data")
Valence & Arousal Survey Data
N = 8321
Education
    Less than high school 4 (0.5%)
    Some college 356 (43%)
    2-year college degree 59 (7.1%)
    4-year college degree 188 (23%)
    Master's degree 52 (6.3%)
    Doctoral's degree 6 (0.7%)
    Professional degree, e.g., JD/MD 11 (1.3%)
    High school/GED/Technical/vocational training) 156 (19%)
Marital Status
    Married, or living as married 169 (20%)
    Divorced or separated 40 (4.8%)
    Widowed 1 (0.1%)
    Single/never married 622 (75%)
Smartphone Hours
    0-1 hours 42 (5.0%)
    1-3 hours 261 (31%)
    3-5 hours 294 (35%)
    5-7 hours 170 (20%)
    More than 7 hours 65 (7.8%)
1 n (%)

Polific Only Demographics

#Prolific Only Demographics
image_ratings_human %>%
  filter(source == "prolific") %>%
  mutate(income = factor(income,
                         levels=c(1,2,3,4,5,6,7,8,9,10,11,12),
                         labels=c("$14,999 or less", "$15,000 - $24,999",
                                  "$25,000 - $29,999","$30,000 - $34,999",
                                  "$35,000 - $49,999", "$50,000 - $74,999",
                                  "$75,000 - $99,999", "$100,000 - $149,999",
                                  "$150,000 - $199,999", "$200,000 or more",
                                  "Don't know", "I prefer not to answer")),
         marital_status = factor(marital_status,
                                 levels=c(1,2,3,4),
                                 labels=c("Married, or living as married",
                                          "Divorced or separated",
                                          "Widowed",
                                          "Single/never married")),
         residence = factor(residence,
                            levels=c(1,2,3),
                            labels=c("Urban", "Suburban", "Rural")),
         region = factor(region, 
                         levels=c(1,2,3,4),
                         labels=c("Northeast", "South", "West", "Midwest"))) %>%
  select(marital_status, income, residence, region) %>%
  tbl_summary(
    label = list(income ~ "Income",
                 marital_status ~ "Marital Status",
                 residence ~ "Residence", region ~ "Region"),
    statistic = list(all_continuous() ~ "{mean} ({min}, {max})"),
    missing = "ifany") %>%
  modify_header(label ~ "") %>%
  as_gt() %>%
  tab_header("Valence & Arousal Survey Data")
Valence & Arousal Survey Data
N = 4511
Marital Status
    Married, or living as married 155 (34%)
    Divorced or separated 34 (7.5%)
    Widowed 0 (0%)
    Single/never married 262 (58%)
Income
    $14,999 or less 44 (9.8%)
    $15,000 - $24,999 28 (6.2%)
    $25,000 - $29,999 24 (5.3%)
    $30,000 - $34,999 27 (6.0%)
    $35,000 - $49,999 60 (13%)
    $50,000 - $74,999 88 (20%)
    $75,000 - $99,999 61 (14%)
    $100,000 - $149,999 64 (14%)
    $150,000 - $199,999 26 (5.8%)
    $200,000 or more 15 (3.3%)
    Don't know 6 (1.3%)
    I prefer not to answer 8 (1.8%)
Residence
    Urban 139 (31%)
    Suburban 252 (56%)
    Rural 60 (13%)
Region
    Northeast 92 (20%)
    South 150 (33%)
    West 116 (26%)
    Midwest 93 (21%)
1 n (%)

Location

#All students were attending suburban, west coast institutions
image_ratings_human <- image_ratings_human %>%
  mutate(residence_all = ifelse(source == "prolific", residence, 2),
         region_all = ifelse(source == "prolific", region, 3))

#Label factor levels
residence_relabel <- function(orig_data){
  val_labels(orig_data) <- 
    c("Urban" = 1, 
      "Suburban" = 2,
      "Rural" = 3
      ) 
  return(orig_data)
}

region_relabel <- function(orig_data){
  val_labels(orig_data) <- 
    c("Northeast" = 1, 
      "South" = 2,
      "West" = 3,
      "Midwest" = 4
      ) 
  return(orig_data)
}

image_ratings_human$residence_all <- residence_relabel(image_ratings_human$residence_all)
image_ratings_human$region_all <- region_relabel(image_ratings_human$region_all)

#Look at location summary
image_ratings_human %>%
  mutate(residence_all = as_factor(residence_all),
         region_all = as_factor(region_all)) %>%
  select(residence_all, region_all) %>%
  tbl_summary(
    label = list(residence_all ~ "Residence", region_all ~ "Region"),
    statistic = list(all_continuous() ~ "{mean} ({min}, {max})"),
    missing = "ifany") %>%
  modify_header(label ~ "") %>%
  as_gt() %>%
  tab_header("Valence & Arousal Survey Data")
Valence & Arousal Survey Data
N = 8321
Residence
    Urban 139 (17%)
    Suburban 633 (76%)
    Rural 60 (7.2%)
Region
    Northeast 92 (11%)
    South 150 (18%)
    West 497 (60%)
    Midwest 93 (11%)
1 n (%)

Valence & Arousal Plots

Valence Plots

#Valence Ratings
image_ratings_all %>%
  ggplot(aes(x = valence_human, y = valence_ml)) +
  geom_smooth(method = "lm", se = T, colour = "grey", linewidth = 0.5, alpha = 0.5) +
  geom_point(colour = "blue") +
  ggtitle("Valence") +
  scale_x_continuous("Human Ratings", limits = c(1,9), n.breaks = 9) +
  scale_y_continuous("ML Ratings", limits = c(1,9), n.breaks = 9) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5, face = "bold")) 

image_ratings_all %>%
  ggplot(aes(x = valence_human, y = valence_ml, color = source)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ggtitle("Valence") +
  scale_x_continuous("Human Ratings", limits = c(1,9), n.breaks = 9) +
  scale_y_continuous("Machine Ratings", limits = c(1,9), n.breaks = 9) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5, face = "bold"))

Arousal Plots

#Arousal Ratings
image_ratings_all %>%
  ggplot(aes(x = arousal_human, y = arousal_ml)) +
  geom_smooth(method = "lm", se = T, colour = "grey", linewidth = 0.5, alpha = 0.5) +
  geom_point(colour = "red") +
  ggtitle("Arousal") +
  scale_x_continuous("Human Ratings", limits = c(1,9), n.breaks = 9) +
  scale_y_continuous("Machine Ratings", limits = c(1,9), n.breaks = 9) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5, face = "bold"))

image_ratings_all %>%
  ggplot(aes(x = arousal_human, y = arousal_ml, color = source)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ggtitle("Arousal") +
  scale_x_continuous("Human Ratings", limits = c(1,9), n.breaks = 9) +
  scale_y_continuous("Machine Ratings", limits = c(1,9), n.breaks = 9) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5, face = "bold"))

Comparing Valence & Arousal

#All by Rater
image_ratings_all_long %>%
  group_by(image) %>%
  ggplot(aes(x = arousal, y = valence, color = raiter)) +
  geom_point() +
  geom_line(aes(group = image), color="grey") +
  ggtitle("Valence and Arousal by Raiter") +
  scale_x_continuous("Arousal", limits = c(1,9), n.breaks = 9) +
  scale_y_continuous("Valence", limits = c(1,9), n.breaks = 9) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5, face = "bold"))

image_ratings_all_long %>%
  group_by(image) %>%
  ggplot(aes(x = arousal, y = valence, color = raiter)) +
  geom_point() +
  geom_line(aes(group = image), color="grey") +
  ggtitle("Valence and Arousal by Raiter") +
  scale_x_continuous("Arousal", limits = c(1,9), n.breaks = 9) +
  scale_y_continuous("Valence", limits = c(1,9), n.breaks = 9) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5, face = "bold")) +
  facet_wrap(~source)

By Image Source

#Arousal
image_ratings_all %>%
  group_by(source) %>%
  ggplot(aes(x = arousal_human, y = arousal_ml)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ggtitle("Arousal") +
  scale_x_continuous("Human Ratings", limits = c(1,9), n.breaks = 9) +
  scale_y_continuous("Machine Ratings", limits = c(1,9), n.breaks = 9) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5, face = "bold")) +
  facet_wrap(~source)

#Valence
image_ratings_all %>%
  group_by(source) %>%
  ggplot(aes(x = valence_human, y = valence_ml)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ggtitle("Valence") +
  scale_x_continuous("Human Ratings", limits = c(1,9), n.breaks = 9) +
  scale_y_continuous("Machine Ratings", limits = c(1,9), n.breaks = 9) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5, face = "bold")) +
  facet_wrap(~source)

#Valence and Arousal
image_ratings_all_long %>%
  group_by(source) %>%
  ggplot(aes(x = arousal, y = valence)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ggtitle("Rating by Source and Raiter") +
  scale_x_continuous("Arousal", limits = c(1,9), n.breaks = 9) +
  scale_y_continuous("Valence", limits = c(1,9), n.breaks = 9) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5, face = "bold")) +
  facet_wrap(~source + raiter)

Av Participant Rating Across Images

Plot showing average participant ratings on valence and arousal across all images.

#Valence
image_ratings_all %>%
  group_by(source) %>%
  ggplot() +
  geom_point(mapping = aes(x = reorder(image, valence_human), 
                           y = valence_human, color = source)) +
  ggtitle("Average Participant Valence Ratings Across Images") +
  xlab("Image") +
  scale_y_continuous("Valence", limits = c(1,9), n.breaks = 9) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5, face = "bold")) +
  theme(axis.text.x=element_blank(),
        axis.ticks.x=element_blank())

#Arousal
image_ratings_all %>%
  group_by(source) %>%
  ggplot() +
  geom_point(mapping = aes(x = reorder(image, arousal_human), 
                           y = arousal_human, color = source)) +
  ggtitle("Average Participant Arousal Ratings Across Images") +
  xlab("Image") +
  scale_y_continuous("Arousal", limits = c(1,9), n.breaks = 9) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5, face = "bold")) +
  theme(axis.text.x=element_blank(),
        axis.ticks.x=element_blank())

#Faceted
image_ratings_all_extralong %>%
  group_by(source) %>%
  ggplot() +
  geom_point(mapping = aes(x = reorder(image, value), 
                           y = value, color = source)) +
  ggtitle("Average Participant Ratings Across Images") +
  xlab("Image") +
  scale_y_continuous("Rating", limits = c(1,9), n.breaks = 9) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5, face = "bold")) +
  theme(axis.text.x=element_blank(),
        axis.ticks.x=element_blank()) +
  facet_wrap(~measure)

Smartphone with Elipses

#All by Rater
image_ratings_all_long %>%
  filter(source == "smartphone") %>%
  mutate(Raiter = ifelse(raiter == "human", "Human", "Machine")) %>%
  group_by(image) %>%
  ggplot(aes(x = arousal, y = valence, color = Raiter)) +
  geom_point(linewidth = 1.5) +
  scale_colour_manual("Ratings", values =
                        c("Human"="#1f77b4", "Machine"="#ff7f0e")) +
  stat_ellipse(linewidth = 1) +
  geom_line(aes(group = image), color="black", size = 0.2) +
  ggtitle("Valence and Arousal:\nSmartphone Images\n") +
  scale_x_continuous("Arousal", limits = c(1,9), n.breaks = 9) +
  scale_y_continuous("Valence", limits = c(1,9), n.breaks = 9) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5, face = "bold"),
        text = element_text(size = 16))

Triangle Plot: OASIS

Plot displaying distance between participant, ML, and previous OASIS ratings on OASIS images.

image_ratings_all %>%
  filter(source=="OASIS")%>%
  ggplot() +
  geom_point(mapping = aes(x = arousal_human, y = valence_human, 
                           colour = "Human"), size = 2) +
  stat_ellipse(mapping = aes(x = arousal_human, y = valence_human, 
                           colour = "Participant"), size = 1) +
  geom_point(mapping = aes(x = arousal_ml, y = valence_ml, 
                           colour = "Human"), size = 2) +
  stat_ellipse(mapping = aes(x = arousal_ml, y = valence_ml, 
                           colour = "Machine"),  size = 1) +
  geom_point(mapping = aes(x = adjusted_arousal, y = adjusted_valence, 
                           colour = "Prior OASIS"), size = 2) + 
  stat_ellipse(mapping = aes(x = adjusted_arousal, y = adjusted_valence, 
                           colour = "Prior OASIS"), size = 1) + 
  scale_colour_manual("Ratings", values = 
                        c("Human"="#1f77b4", 
                          "Machine"="#ff7f0e", 
                          "Prior OASIS"="#2ca02c")) +
  geom_segment(aes(x = arousal_human, y = valence_human, 
                   xend= arousal_ml, yend = valence_ml), 
               linetype = 1, size = 0.5, color ="black") +
  geom_segment(aes(x = arousal_ml, y = valence_ml,
                   xend = adjusted_arousal, yend = adjusted_valence), 
               linetype = 1, size = 0.5, color ="black") +
  geom_segment(aes(x = adjusted_arousal, y = adjusted_valence,
                   xend= arousal_human, yend = valence_human), 
               linetype = 1, size = 0.5, color ="black") +
  scale_x_continuous("Arousal", limits = c(1,9), n.breaks = 9) +
  scale_y_continuous("Valence", limits = c(-1,10), n.breaks = 11) +
  ggtitle("Participant, ML, and Previous \nOASIS Rating Relationships") +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5, face = "bold"),
        text = element_text(size = 16))

Valence & Arousal Data

Data Overview

glimpse(image_ratings_human)
## Rows: 832
## Columns: 221
## $ pid              <int> 1, 10, 100, 101, 102, 103, 104, 105, 106, 107, 108, 1…
## $ source           <chr> "student", "student", "prolific", "student", "student…
## $ age              <int> 19, 36, 31, 28, 20, 54, 28, 20, 22, 19, 19, 36, 28, 2…
## $ race             <int> 6, 3, 1, 1, 1, 1, 1, 1, 1, 7, 4, 1, 1, 4, 1, 5, 1, 3,…
## $ native           <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ asian            <int> NA, 1, NA, NA, NA, NA, NA, NA, NA, 1, NA, NA, NA, NA,…
## $ black            <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA, NA, 1,…
## $ pacific_islander <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ white            <int> NA, NA, 1, 1, 1, 1, 1, 1, 1, 1, NA, 1, 1, NA, 1, NA, …
## $ other_race       <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ pnta_race        <int> 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hispanic         <int> 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2,…
## $ gender           <int> 2, 2, 1, 2, 2, 2, 1, 2, 1, 2, 1, 1, 2, 1, 2, 1, 2, 2,…
## $ residence        <int> NA, NA, 1, NA, NA, 2, NA, 2, NA, NA, NA, 2, 1, 1, 2, …
## $ region           <int> NA, NA, 2, NA, NA, 2, NA, 1, NA, NA, NA, 4, 4, 4, 1, …
## $ education        <int> 8, 3, 4, 3, 2, 4, 2, 2, 4, 2, 2, 4, 4, 8, 2, 2, 4, 4,…
## $ marital_status   <int> 4, 4, 1, 4, 4, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,…
## $ income           <int> NA, NA, 5, NA, NA, 9, NA, 8, NA, NA, NA, 7, 6, 1, 1, …
## $ smartphone_hours <int> 5, 2, 3, 3, 3, 2, 2, 2, 5, 2, 3, 1, 4, 1, 3, 4, 2, 3,…
## $ valence_1        <int> NA, 4, NA, 2, 3, NA, NA, 1, 3, 1, NA, NA, 2, 5, 4, 4,…
## $ valence_2        <int> NA, 4, NA, 4, 2, NA, NA, 3, 2, 3, NA, NA, 5, 5, 4, 6,…
## $ valence_3        <int> NA, 6, NA, 9, 6, NA, NA, 9, 5, 8, NA, NA, 5, 8, 9, 7,…
## $ valence_4        <int> NA, 6, NA, 8, 7, NA, NA, 7, 5, 4, NA, NA, 3, 5, 9, 6,…
## $ valence_5        <int> NA, 4, NA, 2, 5, NA, NA, 4, 4, 4, NA, NA, 2, 3, 3, 5,…
## $ valence_6        <int> NA, 5, NA, 4, 3, NA, NA, 5, 5, 4, NA, NA, 5, 5, 4, 5,…
## $ valence_7        <int> NA, 5, NA, 5, 4, NA, NA, 5, 4, 5, NA, NA, 5, 4, 4, 5,…
## $ valence_8        <int> NA, 5, NA, 5, 1, NA, NA, 5, 5, 5, NA, NA, 2, 5, 4, 5,…
## $ valence_9        <int> NA, 5, NA, 4, 3, NA, NA, 5, 5, 4, NA, NA, 5, 5, 4, 5,…
## $ valence_10       <int> NA, 6, NA, 6, 7, NA, NA, 6, 6, 6, NA, NA, 5, 6, 4, 6,…
## $ valence_11       <int> NA, 7, NA, 7, 6, NA, NA, 5, 6, 5, NA, NA, 5, 6, 9, 5,…
## $ valence_12       <int> NA, 6, NA, 5, 6, NA, NA, 5, 5, 3, NA, NA, 6, 5, 5, 5,…
## $ valence_13       <int> NA, 1, NA, 3, 2, NA, NA, 5, 8, 2, NA, NA, 5, 7, 1, 5,…
## $ valence_14       <int> NA, 2, NA, 2, 1, NA, NA, 6, 6, 1, NA, NA, 4, 7, 1, 5,…
## $ valence_15       <int> NA, 4, NA, 5, 2, NA, NA, 5, 8, 1, NA, NA, 3, 6, 5, 5,…
## $ valence_16       <int> NA, 4, NA, 6, 2, NA, NA, 6, 6, 3, NA, NA, 5, 6, 3, 5,…
## $ valence_17       <int> NA, 3, NA, 2, 1, NA, NA, 1, 3, 1, NA, NA, 1, 2, 1, 2,…
## $ valence_18       <int> NA, 3, NA, 4, 2, NA, NA, 6, 9, 2, NA, NA, 4, 7, 1, 5,…
## $ valence_19       <int> NA, 6, NA, 7, 7, NA, NA, 6, 6, 6, NA, NA, 7, 6, 9, 6,…
## $ valence_20       <int> NA, 5, NA, 5, 3, NA, NA, 5, 3, 2, NA, NA, 4, 6, 4, 4,…
## $ valence_21       <int> NA, 6, NA, 6, 7, NA, NA, 7, 8, 7, NA, NA, 8, 7, 9, 7,…
## $ valence_22       <int> NA, 5, NA, 3, 5, NA, NA, 6, 9, 2, NA, NA, 9, 5, 5, 5,…
## $ valence_23       <int> NA, 4, NA, 5, 1, NA, NA, 1, 5, 5, NA, NA, 5, 3, 5, 5,…
## $ valence_24       <int> NA, 4, NA, 4, 5, NA, NA, 2, 4, 4, NA, NA, 2, 2, 4, 4,…
## $ valence_25       <int> NA, 4, NA, 3, 3, NA, NA, 3, 2, 3, NA, NA, 2, 5, 3, 2,…
## $ valence_26       <int> NA, 4, NA, 4, 5, NA, NA, 5, 3, 6, NA, NA, 5, 5, 5, 5,…
## $ valence_27       <int> NA, 5, NA, 5, 4, NA, NA, 5, 6, 6, NA, NA, 3, 5, 9, 5,…
## $ valence_28       <int> NA, 4, NA, 9, 8, NA, NA, 7, 6, 9, NA, NA, 6, 6, 9, 7,…
## $ valence_29       <int> NA, 2, NA, 1, 2, NA, NA, 1, 2, 1, NA, NA, 1, 2, 1, 2,…
## $ valence_30       <int> NA, 6, NA, 5, 6, NA, NA, 5, 5, 6, NA, NA, 5, 6, 5, 5,…
## $ valence_31       <int> NA, 5, NA, 4, 2, NA, NA, 5, 5, 3, NA, NA, 5, 3, 5, 5,…
## $ valence_32       <int> NA, 5, NA, 5, 6, NA, NA, 5, 5, 5, NA, NA, 5, 7, 5, 5,…
## $ valence_33       <int> NA, 3, NA, 4, 4, NA, NA, 4, 5, 1, NA, NA, 3, 4, 5, 4,…
## $ valence_34       <int> NA, 5, NA, 5, 4, NA, NA, 4, 6, 5, NA, NA, 2, 5, 4, 4,…
## $ valence_35       <int> NA, 5, NA, 5, 5, NA, NA, 5, 5, 5, NA, NA, 2, 6, 9, 7,…
## $ valence_36       <int> NA, 6, NA, 5, 5, NA, NA, 5, 5, 3, NA, NA, 5, 6, 9, 5,…
## $ valence_37       <int> NA, 6, NA, 7, 7, NA, NA, 8, 6, 5, NA, NA, 2, 7, 9, 7,…
## $ valence_38       <int> NA, 2, NA, 1, 2, NA, NA, 1, 2, 1, NA, NA, 1, 3, 1, 1,…
## $ valence_39       <int> NA, 2, NA, 1, 3, NA, NA, 2, 4, 4, NA, NA, 5, 2, 1, 2,…
## $ valence_40       <int> NA, 5, NA, 5, 5, NA, NA, 5, 5, 3, NA, NA, 5, 5, 5, 5,…
## $ valence_41       <int> NA, 1, NA, 2, 3, NA, NA, 5, 8, 1, NA, NA, 3, 8, 1, 5,…
## $ valence_42       <int> NA, 1, NA, 3, 3, NA, NA, 6, 5, 2, NA, NA, 2, 5, 3, 7,…
## $ valence_43       <int> NA, 1, NA, 2, 1, NA, NA, 5, 6, 1, NA, NA, 4, 8, 1, 5,…
## $ valence_44       <int> NA, 5, NA, 6, 6, NA, NA, 5, 5, 5, NA, NA, 5, 5, 5, 5,…
## $ valence_45       <int> NA, 3, NA, 5, 6, NA, NA, 2, 6, 5, NA, NA, 2, 3, 2, 7,…
## $ valence_46       <int> NA, 4, NA, 5, 6, NA, NA, 3, 6, 4, NA, NA, 1, 6, 2, 6,…
## $ valence_47       <int> NA, 4, NA, 4, 4, NA, NA, 4, 2, 6, NA, NA, 3, 5, 6, 4,…
## $ valence_48       <int> NA, 4, NA, 3, 3, NA, NA, 5, 4, 1, NA, NA, 3, 5, 5, 8,…
## $ valence_49       <int> NA, 4, NA, 6, 5, NA, NA, 5, 6, 2, NA, NA, 5, 5, 5, 5,…
## $ valence_50       <int> NA, 1, NA, 1, 1, NA, NA, 1, 2, 1, NA, NA, 3, 2, 1, 1,…
## $ valence_51       <int> 4, 4, 5, 7, NA, 9, 6, NA, 3, 9, 5, 6, NA, NA, 3, 4, 7…
## $ valence_52       <int> 9, 9, 8, 9, NA, 9, 9, NA, 8, 9, 9, 8, NA, NA, 4, 6, 9…
## $ valence_53       <int> 1, 1, 3, 1, NA, 1, 3, NA, 2, 1, 3, 3, NA, NA, 1, 3, 2…
## $ valence_54       <int> 4, 4, 4, 1, NA, 2, 8, NA, 2, 2, 6, 3, NA, NA, 2, 3, 4…
## $ valence_55       <int> 8, 5, 3, 1, NA, 3, 2, NA, 5, 4, 7, 5, NA, NA, 5, 5, 6…
## $ valence_56       <int> 4, 5, 4, 5, NA, 3, 5, NA, 5, 3, 3, 5, NA, NA, 5, 5, 5…
## $ valence_57       <int> 7, 6, 4, 1, NA, 5, 4, NA, 5, 6, 7, 5, NA, NA, 5, 5, 8…
## $ valence_58       <int> 8, 7, 5, 1, NA, 5, 8, NA, 6, 5, 6, 7, NA, NA, 5, 5, 7…
## $ valence_59       <int> 7, 7, 5, 5, NA, 9, 3, NA, 6, 2, 4, 6, NA, NA, 6, 7, 5…
## $ valence_60       <int> 5, 4, 4, 4, NA, 6, 3, NA, 6, 5, 5, 5, NA, NA, 5, 5, 4…
## $ valence_61       <int> 1, 4, 3, 4, NA, 3, 1, NA, 3, 1, 5, 1, NA, NA, 1, 2, 2…
## $ valence_62       <int> 5, 4, 6, 9, NA, 8, 7, NA, 6, 5, 4, 7, NA, NA, 6, 5, 6…
## $ valence_63       <int> 5, 5, 4, 1, NA, 5, 3, NA, 5, 3, 3, 4, NA, NA, 5, 5, 5…
## $ valence_64       <int> 1, 1, 3, 1, NA, 1, 3, NA, 1, 1, 3, 1, NA, NA, 1, 1, 2…
## $ valence_65       <int> 8, 5, 6, 7, NA, 2, 7, NA, 5, 1, 6, 5, NA, NA, 4, 5, 8…
## $ valence_66       <int> 2, 2, 2, 2, NA, 1, 4, NA, 2, 4, 6, 3, NA, NA, 2, 1, 2…
## $ valence_67       <int> 5, 5, 3, 1, NA, 1, 7, NA, 5, 4, 6, 5, NA, NA, 5, 3, 7…
## $ valence_68       <int> 5, 5, 3, 1, NA, 2, 1, NA, 5, 4, 7, 5, NA, NA, 5, 5, 6…
## $ valence_69       <int> 3, 4, 3, 1, NA, 2, 4, NA, 2, 3, 3, 3, NA, NA, 1, 3, 2…
## $ valence_70       <int> 1, 6, 3, 1, NA, 5, 4, NA, 5, 1, 7, 5, NA, NA, 2, 1, 3…
## $ valence_71       <int> 5, 5, 6, 1, NA, 7, 4, NA, 5, 5, 6, 5, NA, NA, 5, 7, 5…
## $ valence_72       <int> 5, 5, 5, 1, NA, 1, 3, NA, 5, 4, 5, 4, NA, NA, 5, 5, 5…
## $ valence_73       <int> 4, 4, 5, 4, NA, 4, 5, NA, 4, 1, 6, 4, NA, NA, 5, 3, 6…
## $ valence_74       <int> 4, 4, 5, 1, NA, 4, 5, NA, 4, 4, 5, 3, NA, NA, 4, 3, 5…
## $ valence_75       <int> 5, 5, 3, 5, NA, 5, 5, NA, 5, 5, 5, 6, NA, NA, 5, 5, 6…
## $ valence_76       <int> 6, 5, 5, 5, NA, 6, 5, NA, 6, 6, 7, 7, NA, NA, 5, 5, 7…
## $ valence_77       <int> 7, 5, 5, 6, NA, 6, 6, NA, 6, 4, 7, 6, NA, NA, 5, 5, 7…
## $ valence_78       <int> 7, 6, 5, 7, NA, 2, 7, NA, 7, 6, 7, 5, NA, NA, 5, 5, 7…
## $ valence_79       <int> 6, 4, 6, 1, NA, 2, 5, NA, 7, 1, 7, 7, NA, NA, 3, 5, 5…
## $ valence_80       <int> 9, 5, 5, 5, NA, 5, 5, NA, 6, 5, 4, 6, NA, NA, 5, 7, 7…
## $ valence_81       <int> 1, 1, 1, 1, NA, 1, 7, NA, 1, 1, 1, 3, NA, NA, 1, 1, 1…
## $ valence_82       <int> 5, 5, 7, 6, NA, 3, 7, NA, 6, 5, 7, 5, NA, NA, 6, 4, 7…
## $ valence_83       <int> 9, 5, 3, 1, NA, 2, 2, NA, 6, 1, 6, 5, NA, NA, 5, 5, 7…
## $ valence_84       <int> 5, 5, 5, 4, NA, 5, 5, NA, 6, 1, 4, 5, NA, NA, 5, 8, 4…
## $ valence_85       <int> 3, 4, 5, 5, NA, 3, 7, NA, 5, 5, 6, 5, NA, NA, 5, 7, 5…
## $ valence_86       <int> 5, 4, 6, 5, NA, 7, 5, NA, 7, 4, 4, 5, NA, NA, 5, 6, 5…
## $ valence_87       <int> 5, 1, 5, 4, NA, 2, 2, NA, 7, 1, 4, 5, NA, NA, 1, 6, 3…
## $ valence_88       <int> 5, 2, 5, 4, NA, 2, 3, NA, 7, 1, 4, 7, NA, NA, 1, 7, 5…
## $ valence_89       <int> 5, 1, 5, 1, NA, 1, 3, NA, 9, 2, 5, 7, NA, NA, 2, 5, 6…
## $ valence_90       <int> 4, 5, 5, 5, NA, 5, 8, NA, 5, 4, 5, 5, NA, NA, 5, 5, 5…
## $ valence_91       <int> 7, 5, 5, 7, NA, 7, 6, NA, 9, 2, 5, 6, NA, NA, 5, 5, 8…
## $ valence_92       <int> 5, 5, 6, 2, NA, 8, 5, NA, 7, 4, 4, 7, NA, NA, 5, 6, 4…
## $ valence_93       <int> 9, 5, 7, 7, NA, 5, 5, NA, 9, 5, 7, 8, NA, NA, 5, 4, 3…
## $ valence_94       <int> 3, 2, 5, 3, NA, 1, 3, NA, 5, 3, 5, 5, NA, NA, 2, 7, 4…
## $ valence_95       <int> 8, 4, 2, 4, NA, 3, 5, NA, 4, 3, 3, 5, NA, NA, 5, 2, 4…
## $ valence_96       <int> 3, 4, 4, 1, NA, 1, 2, NA, 8, 1, 5, 5, NA, NA, 1, 5, 5…
## $ valence_97       <int> 7, 6, 2, 2, NA, 3, 6, NA, 2, 4, 4, 5, NA, NA, 5, 5, 8…
## $ valence_98       <int> 5, 4, 7, 1, NA, 1, 5, NA, 8, 1, 5, 6, NA, NA, 4, 5, 5…
## $ valence_99       <int> 6, 5, 5, 1, NA, 6, 5, NA, 3, 4, 4, 4, NA, NA, 3, 3, 7…
## $ valence_100      <int> 8, 7, 7, 8, NA, 5, 8, NA, 7, 7, 8, 5, NA, NA, 7, 8, 8…
## $ arousal_1        <int> NA, 4, NA, 1, 7, NA, NA, 9, 7, 8, NA, NA, 7, 1, 6, 2,…
## $ arousal_2        <int> NA, 5, NA, 5, 6, NA, NA, 6, 5, 2, NA, NA, 3, 1, 1, 4,…
## $ arousal_3        <int> NA, 5, NA, 9, 2, NA, NA, 5, 3, 7, NA, NA, 3, 6, 5, 5,…
## $ arousal_4        <int> NA, 5, NA, 5, 4, NA, NA, 5, 4, 1, NA, NA, 2, 1, 1, 6,…
## $ arousal_5        <int> NA, 2, NA, 1, 1, NA, NA, 8, 6, 3, NA, NA, 5, 1, 1, 1,…
## $ arousal_6        <int> NA, 5, NA, 5, 6, NA, NA, 1, 1, 5, NA, NA, 2, 5, 1, 1,…
## $ arousal_7        <int> NA, 5, NA, 2, 2, NA, NA, 1, 3, 1, NA, NA, 3, 5, 1, 5,…
## $ arousal_8        <int> NA, 5, NA, 5, 7, NA, NA, 1, 2, 6, NA, NA, 2, 5, 1, 1,…
## $ arousal_9        <int> NA, 5, NA, 5, 5, NA, NA, 1, 2, 5, NA, NA, 5, 5, 1, 5,…
## $ arousal_10       <int> NA, 5, NA, 5, 6, NA, NA, 4, 5, 5, NA, NA, 5, 6, 1, 6,…
## $ arousal_11       <int> NA, 6, NA, 5, 5, NA, NA, 1, 4, 5, NA, NA, 4, 6, 2, 1,…
## $ arousal_12       <int> NA, 6, NA, 5, 5, NA, NA, 1, 4, 2, NA, NA, 3, 5, 1, 5,…
## $ arousal_13       <int> NA, 3, NA, 1, 7, NA, NA, 6, 7, 8, NA, NA, 3, 9, 1, 1,…
## $ arousal_14       <int> NA, 2, NA, 1, 9, NA, NA, 6, 8, 7, NA, NA, 5, 9, 1, 1,…
## $ arousal_15       <int> NA, 1, NA, 1, 7, NA, NA, 6, 6, 8, NA, NA, 5, 7, 1, 5,…
## $ arousal_16       <int> NA, 5, NA, 1, 4, NA, NA, 6, 8, 6, NA, NA, 4, 6, 1, 5,…
## $ arousal_17       <int> NA, 6, NA, 1, 3, NA, NA, 9, 6, 9, NA, NA, 7, 1, 9, 8,…
## $ arousal_18       <int> NA, 2, NA, 1, 8, NA, NA, 6, 8, 2, NA, NA, 4, 8, 1, 1,…
## $ arousal_19       <int> NA, 6, NA, 7, 6, NA, NA, 5, 5, 9, NA, NA, 8, 4, 7, 6,…
## $ arousal_20       <int> NA, 5, NA, 4, 2, NA, NA, 1, 1, 1, NA, NA, 1, 5, 1, 4,…
## $ arousal_21       <int> NA, 5, NA, 6, 6, NA, NA, 5, 5, 9, NA, NA, 3, 6, 1, 5,…
## $ arousal_22       <int> NA, 5, NA, 2, 1, NA, NA, 5, 4, 2, NA, NA, 8, 1, 1, 2,…
## $ arousal_23       <int> NA, 4, NA, 5, 3, NA, NA, 7, 4, 6, NA, NA, 5, 1, 1, 1,…
## $ arousal_24       <int> NA, 2, NA, 1, 5, NA, NA, 7, 3, 3, NA, NA, 6, 1, 1, 1,…
## $ arousal_25       <int> NA, 4, NA, 1, 5, NA, NA, 8, 7, 9, NA, NA, 6, 1, 1, 7,…
## $ arousal_26       <int> NA, 4, NA, 1, 5, NA, NA, 2, 6, 6, NA, NA, 3, 1, 1, 1,…
## $ arousal_27       <int> NA, 5, NA, 6, 1, NA, NA, 1, 4, 7, NA, NA, 5, 1, 1, 1,…
## $ arousal_28       <int> NA, 5, NA, 9, 8, NA, NA, 6, 4, 9, NA, NA, 5, 5, 8, 6,…
## $ arousal_29       <int> NA, 6, NA, 1, 5, NA, NA, 9, 6, 9, NA, NA, 6, 1, 6, 7,…
## $ arousal_30       <int> NA, 5, NA, 1, 6, NA, NA, 1, 4, 5, NA, NA, 3, 5, 1, 1,…
## $ arousal_31       <int> NA, 5, NA, 1, 5, NA, NA, 1, 2, 1, NA, NA, 1, 5, 1, 5,…
## $ arousal_32       <int> NA, 5, NA, 7, 5, NA, NA, 1, 3, 1, NA, NA, 2, 1, 1, 1,…
## $ arousal_33       <int> NA, 6, NA, 4, 6, NA, NA, 7, 6, 9, NA, NA, 6, 1, 1, 6,…
## $ arousal_34       <int> NA, 5, NA, 1, 2, NA, NA, 7, 4, 5, NA, NA, 5, 1, 1, 3,…
## $ arousal_35       <int> NA, 5, NA, 1, 5, NA, NA, 2, 4, 3, NA, NA, 3, 1, 1, 7,…
## $ arousal_36       <int> NA, 5, NA, 1, 1, NA, NA, 5, 6, 5, NA, NA, 5, 6, 1, 1,…
## $ arousal_37       <int> NA, 5, NA, 1, 6, NA, NA, 7, 5, 4, NA, NA, 4, 1, 7, 7,…
## $ arousal_38       <int> NA, 6, NA, 1, 8, NA, NA, 9, 6, 9, NA, NA, 8, 1, 9, 9,…
## $ arousal_39       <int> NA, 7, NA, 1, 5, NA, NA, 8, 7, 7, NA, NA, 6, 1, 1, 8,…
## $ arousal_40       <int> NA, 3, NA, 6, 2, NA, NA, 2, 3, 1, NA, NA, 3, 1, 1, 1,…
## $ arousal_41       <int> NA, 5, NA, 1, 5, NA, NA, 6, 8, 7, NA, NA, 3, 9, 1, 5,…
## $ arousal_42       <int> NA, 6, NA, 5, 4, NA, NA, 6, 9, 7, NA, NA, 6, 6, 1, 7,…
## $ arousal_43       <int> NA, 5, NA, 1, 7, NA, NA, 6, 8, 4, NA, NA, 3, 9, 1, 1,…
## $ arousal_44       <int> NA, 5, NA, 5, 3, NA, NA, 1, 1, 1, NA, NA, 5, 5, 1, 1,…
## $ arousal_45       <int> NA, 5, NA, 1, 4, NA, NA, 9, 4, 6, NA, NA, 7, 1, 6, 6,…
## $ arousal_46       <int> NA, 4, NA, 1, 6, NA, NA, 8, 4, 5, NA, NA, 8, 1, 1, 6,…
## $ arousal_47       <int> NA, 6, NA, 6, 6, NA, NA, 7, 8, 7, NA, NA, 6, 1, 1, 7,…
## $ arousal_48       <int> NA, 6, NA, 5, 7, NA, NA, 5, 4, 4, NA, NA, 3, 5, 1, 8,…
## $ arousal_49       <int> NA, 5, NA, 2, 2, NA, NA, 2, 5, 8, NA, NA, 3, 5, 1, 1,…
## $ arousal_50       <int> NA, 6, NA, 1, 8, NA, NA, 9, 7, 9, NA, NA, 6, 1, 6, 9,…
## $ arousal_51       <int> 7, 5, 4, 1, NA, 8, 6, NA, 7, 6, 7, 6, NA, NA, 1, 4, 5…
## $ arousal_52       <int> 9, 9, 7, 9, NA, 6, 7, NA, 7, 9, 9, 8, NA, NA, 1, 6, 7…
## $ arousal_53       <int> 9, 6, 8, 1, NA, 9, 7, NA, 9, 9, 8, 8, NA, NA, 6, 6, 2…
## $ arousal_54       <int> 8, 5, 4, 4, NA, 5, 5, NA, 8, 1, 7, 6, NA, NA, 2, 6, 5…
## $ arousal_55       <int> 2, 5, 3, 1, NA, 5, 2, NA, 3, 3, 3, 3, NA, NA, 1, 1, 5…
## $ arousal_56       <int> 6, 5, 2, 5, NA, 5, 3, NA, 1, 2, 6, 3, NA, NA, 1, 1, 5…
## $ arousal_57       <int> 2, 5, 4, 1, NA, 5, 2, NA, 3, 4, 4, 2, NA, NA, 4, 3, 6…
## $ arousal_58       <int> 4, 6, 4, 6, NA, 5, 6, NA, 3, 4, 6, 6, NA, NA, 1, 2, 5…
## $ arousal_59       <int> 7, 4, 6, 1, NA, 7, 3, NA, 4, 6, 3, 2, NA, NA, 1, 7, 2…
## $ arousal_60       <int> 1, 5, 5, 1, NA, 5, 5, NA, 4, 1, 6, 6, NA, NA, 1, 3, 3…
## $ arousal_61       <int> 9, 6, 6, 1, NA, 8, 7, NA, 6, 9, 6, 8, NA, NA, 7, 9, 5…
## $ arousal_62       <int> 5, 4, 6, 1, NA, 5, 4, NA, 6, 6, 7, 6, NA, NA, 4, 1, 5…
## $ arousal_63       <int> 5, 5, 1, 5, NA, 5, 1, NA, 1, 1, 1, 1, NA, NA, 1, 1, 5…
## $ arousal_64       <int> 9, 7, 8, NA, NA, 7, 7, NA, 9, 9, 9, 8, NA, NA, 9, 6, …
## $ arousal_65       <int> 6, 5, 6, 9, NA, 5, 6, NA, 7, 9, 5, 6, NA, NA, 1, 1, 6…
## $ arousal_66       <int> 8, 5, 5, 5, NA, 2, 7, NA, 8, 7, 6, 6, NA, NA, 1, 8, 4…
## $ arousal_67       <int> 4, 5, 3, 5, NA, 6, 2, NA, 1, 4, 5, 3, NA, NA, 1, 6, 4…
## $ arousal_68       <int> 5, 5, 5, 1, NA, 4, 2, NA, 3, 5, 3, 2, NA, NA, 1, 1, 5…
## $ arousal_69       <int> 8, 6, 6, 1, NA, 6, 4, NA, 7, 8, 7, 7, NA, NA, 1, 7, 5…
## $ arousal_70       <int> 9, 6, 6, 1, NA, 5, 3, NA, 6, 8, 6, 5, NA, NA, 4, 8, 6…
## $ arousal_71       <int> 7, 5, 2, 5, NA, 7, 4, NA, 2, 5, 6, 2, NA, NA, 1, 6, 5…
## $ arousal_72       <int> 2, 5, 2, 5, NA, 2, 3, NA, 1, 3, 1, 1, NA, NA, 1, 1, 3…
## $ arousal_73       <int> 7, 6, 7, 1, NA, 7, 7, NA, 7, 6, 6, 6, NA, NA, 1, 6, 5…
## $ arousal_74       <int> 6, 5, 2, 5, NA, 6, 5, NA, 4, 6, 5, 7, NA, NA, 1, 4, 5…
## $ arousal_75       <int> 1, 5, 4, 1, NA, 5, 5, NA, 2, 5, 3, 2, NA, NA, 1, 1, 5…
## $ arousal_76       <int> 5, 4, 4, 5, NA, 5, 5, NA, 4, 6, 6, 3, NA, NA, 1, 1, 5…
## $ arousal_77       <int> 5, 5, 4, 5, NA, 6, 5, NA, 5, 4, 5, 6, NA, NA, 1, 1, 6…
## $ arousal_78       <int> 5, 5, 7, 1, NA, 3, 3, NA, 6, 6, 3, 6, NA, NA, 1, 1, 6…
## $ arousal_79       <int> 5, 5, 6, 1, NA, 6, 8, NA, 8, 6, 6, 7, NA, NA, 1, 1, 6…
## $ arousal_80       <int> 7, 5, 4, 1, NA, 5, 5, NA, 5, 5, 3, 5, NA, NA, 1, 6, 5…
## $ arousal_81       <int> 8, 6, 8, 1, NA, 1, 7, NA, 9, 8, 9, 6, NA, NA, 6, 8, 1…
## $ arousal_82       <int> 5, 5, 5, 5, NA, 8, 6, NA, 5, 5, 6, 6, NA, NA, 5, 3, 5…
## $ arousal_83       <int> 3, 5, 5, 1, NA, 3, 2, NA, 4, 7, 2, 3, NA, NA, 1, 1, 5…
## $ arousal_84       <int> 5, 5, 4, 1, NA, 5, 3, NA, 4, 7, 5, 4, NA, NA, 1, 7, 5…
## $ arousal_85       <int> 6, 5, 4, 5, NA, 4, 7, NA, 6, 5, 6, 6, NA, NA, 1, 7, 5…
## $ arousal_86       <int> 5, 5, 8, 5, NA, 5, 3, NA, 5, 8, 6, 6, NA, NA, 1, 4, 5…
## $ arousal_87       <int> 5, 6, 5, 2, NA, 5, 7, NA, 8, 9, 5, 8, NA, NA, 1, 5, 2…
## $ arousal_88       <int> 5, 5, 4, 1, NA, 2, 7, NA, 8, 8, 6, 8, NA, NA, 1, 6, 5…
## $ arousal_89       <int> 5, 5, 6, 1, NA, 6, 6, NA, 9, 8, 5, 8, NA, NA, 1, 1, 6…
## $ arousal_90       <int> 5, 5, 3, 5, NA, 5, 4, NA, 2, 3, 2, 3, NA, NA, 1, 1, 5…
## $ arousal_91       <int> 5, 4, 4, 1, NA, 5, 5, NA, 7, 6, 5, 5, NA, NA, 1, 1, 6…
## $ arousal_92       <int> 5, 6, 6, 1, NA, 7, 6, NA, 6, 5, 7, 5, NA, NA, 1, 7, 5…
## $ arousal_93       <int> 6, 5, 5, 5, NA, 2, 3, NA, 6, 5, 8, 8, NA, NA, 1, 3, 1…
## $ arousal_94       <int> 5, 6, 7, 1, NA, 1, 6, NA, 9, 7, 7, 6, NA, NA, 1, 7, 3…
## $ arousal_95       <int> 6, 6, 9, 1, NA, 5, 7, NA, 8, 7, 7, 7, NA, NA, 1, 8, 3…
## $ arousal_96       <int> 5, 5, 5, 1, NA, 5, 6, NA, 6, 8, 6, 6, NA, NA, 1, 1, 4…
## $ arousal_97       <int> 5, 5, 7, 1, NA, 5, 6, NA, 8, 8, 7, 7, NA, NA, 1, 1, 5…
## $ arousal_98       <int> 5, 5, 8, 3, NA, 1, 6, NA, 7, 8, 5, 8, NA, NA, 1, 1, 6…
## $ arousal_99       <int> 5, 5, 7, 1, NA, 6, 6, NA, 7, 7, 6, 7, NA, NA, 1, 5, 5…
## $ arousal_100      <int> 2, 4, 6, 9, NA, 5, 3, NA, 4, 6, 3, 3, NA, NA, 1, 8, 6…
## $ residence_all    <dbl+lbl> 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2…
## $ region_all       <dbl+lbl> 3, 3, 2, 3, 3, 2, 3, 1, 3, 3, 3, 4, 4, 4, 1, 3, 2…
kbl(describe(image_ratings_human[,c(20:219)])) %>%
  kable_styling(bootstrap_options = c("striped", "hover"), 
                full_width = F,
                fixed_thead = F)
vars n mean sd median trimmed mad min max range skew kurtosis se
valence_1 1 541 3.349353 1.5073722 3.0 3.270208 1.4826 1 9 8 0.6001275 0.6034164 0.0648070
valence_2 2 541 3.628466 1.1613543 4.0 3.653580 1.4826 1 8 7 0.0100716 0.4366718 0.0499305
valence_3 3 539 7.220779 1.4238589 7.0 7.355658 1.4826 1 9 8 -1.1782227 2.6186488 0.0613299
valence_4 4 540 5.709259 1.7041659 6.0 5.773148 1.4826 1 9 8 -0.3129542 0.0186661 0.0733356
valence_5 5 540 4.674074 1.6518034 5.0 4.666667 1.4826 1 9 8 0.1021700 0.0499273 0.0710823
valence_6 6 541 4.731978 0.8945688 5.0 4.817552 0.0000 1 9 8 -0.6432281 4.9746022 0.0384605
valence_7 7 541 4.741220 1.1236274 5.0 4.815243 0.0000 1 9 8 -0.5281457 3.1761768 0.0483085
valence_8 8 541 4.563771 0.9879364 5.0 4.683603 0.0000 1 9 8 -0.9649011 3.0662846 0.0424747
valence_9 9 541 4.680222 0.9125446 5.0 4.799076 0.0000 1 8 7 -1.0652033 3.6193454 0.0392334
valence_10 10 541 5.970425 1.4359968 6.0 5.986143 1.4826 1 9 8 -0.3303338 1.1790044 0.0617383
valence_11 11 541 6.051756 1.4224072 6.0 6.057737 1.4826 1 9 8 -0.1950718 0.6843469 0.0611541
valence_12 12 541 5.711645 1.4472302 6.0 5.706697 1.4826 1 9 8 -0.1930798 1.0563398 0.0622213
valence_13 13 540 4.831481 2.0192326 5.0 4.821759 1.4826 1 9 8 0.0208343 -0.4423392 0.0868939
valence_14 14 541 4.624769 2.1597445 4.0 4.545035 2.9652 1 9 8 0.2592631 -0.6809424 0.0928547
valence_15 15 541 5.367837 1.5961762 5.0 5.385681 1.4826 1 9 8 -0.1973276 0.4925717 0.0686250
valence_16 16 541 5.384473 1.5456208 5.0 5.406466 1.4826 1 9 8 -0.0914562 0.3706683 0.0664514
valence_17 17 541 2.035120 1.2385271 2.0 1.836028 1.4826 1 9 8 1.9008363 5.9431645 0.0532484
valence_18 18 541 5.454714 1.8639566 5.0 5.464203 1.4826 1 9 8 -0.0274533 -0.4120613 0.0801378
valence_19 19 541 6.377079 1.5135657 6.0 6.459584 1.4826 1 9 8 -0.7294959 1.2968182 0.0650733
valence_20 20 541 4.319778 1.1798292 5.0 4.468822 0.0000 1 9 8 -0.8123874 1.3418300 0.0507248
valence_21 21 541 6.741220 1.4101292 7.0 6.780601 1.4826 1 9 8 -0.6598813 1.2185605 0.0606262
valence_22 22 540 5.066667 1.5371709 5.0 5.134259 1.4826 1 9 8 -0.3869126 0.7905558 0.0661493
valence_23 23 540 3.411111 1.6585827 3.0 3.358796 1.4826 1 9 8 0.2604819 -0.4756324 0.0713740
valence_24 24 541 3.508318 1.6606628 3.0 3.411085 1.4826 1 9 8 0.5702814 0.3158323 0.0713975
valence_25 25 541 3.249538 1.6177902 3.0 3.166282 1.4826 1 8 7 0.4132266 -0.4332239 0.0695542
valence_26 26 540 4.811111 1.3962136 5.0 4.861111 1.4826 1 9 8 -0.3068134 1.4323408 0.0600835
valence_27 27 540 5.655556 1.4745417 6.0 5.659722 1.4826 1 9 8 -0.2408259 1.0095537 0.0634542
valence_28 28 540 7.377778 1.5282943 8.0 7.574074 1.4826 1 9 8 -1.1350468 1.7092675 0.0657673
valence_29 29 540 2.118518 1.2590414 2.0 1.935185 1.4826 1 9 8 1.2185776 1.7661504 0.0541805
valence_30 30 540 5.411111 1.3489041 5.0 5.402778 1.4826 1 9 8 -0.0989097 2.0350313 0.0580476
valence_31 31 540 4.503704 1.1581706 5.0 4.604167 0.0000 1 9 8 -0.7242003 2.2374773 0.0498397
valence_32 32 540 5.001852 0.8582224 5.0 5.020833 0.0000 1 9 8 -0.8824084 9.7503863 0.0369320
valence_33 33 541 3.924214 1.7637779 4.0 3.898383 1.4826 1 9 8 0.2099316 -0.3684396 0.0758307
valence_34 34 540 4.177778 1.3599993 4.0 4.231482 1.4826 1 9 8 -0.2302302 0.7820723 0.0585251
valence_35 35 540 6.055556 1.5769286 6.0 6.076389 1.4826 1 9 8 -0.3378847 0.8812250 0.0678602
valence_36 36 541 5.207024 1.4329543 5.0 5.228637 1.4826 1 9 8 -0.2371204 1.6428786 0.0616075
valence_37 37 540 6.946296 1.6032450 7.0 7.069444 1.4826 1 9 8 -0.8240173 0.9849065 0.0689927
valence_38 38 540 1.524074 1.0397365 1.0 1.280093 0.0000 1 9 8 2.8818128 11.8304994 0.0447431
valence_39 39 541 3.284658 1.8728878 3.0 3.117783 1.4826 1 9 8 0.6246140 -0.0077511 0.0805217
valence_40 40 540 5.637037 1.2942425 5.0 5.648148 1.4826 1 9 8 -0.4228844 2.3619679 0.0556953
valence_41 41 541 4.988909 2.1195446 5.0 4.981524 1.4826 1 9 8 0.0214924 -0.5970282 0.0911263
valence_42 42 540 4.324074 1.9235322 4.0 4.280093 1.4826 1 9 8 0.1966414 -0.3848232 0.0827756
valence_43 43 540 4.661111 2.1984070 5.0 4.615741 2.9652 1 9 8 0.1604229 -0.7194946 0.0946044
valence_44 44 541 5.025878 0.9305889 5.0 5.011547 0.0000 1 9 8 0.0176291 7.4636251 0.0400091
valence_45 45 539 3.775510 1.9746708 4.0 3.651270 1.4826 1 9 8 0.4298373 -0.4220516 0.0850551
valence_46 46 540 4.337037 1.7794223 4.0 4.300926 1.4826 1 9 8 0.1366322 -0.2490410 0.0765741
valence_47 47 540 3.935185 2.0543323 4.0 3.840278 2.9652 1 9 8 0.2805496 -0.6688910 0.0884044
valence_48 48 541 4.850277 1.5437283 5.0 4.840647 1.4826 1 9 8 0.0004210 0.6043703 0.0663701
valence_49 49 540 4.659259 1.6577830 5.0 4.699074 1.4826 1 9 8 -0.1505609 0.2923431 0.0713396
valence_50 50 540 2.001852 1.2929078 2.0 1.773148 1.4826 1 9 8 1.6365740 3.5284329 0.0556379
valence_51 51 535 4.837383 1.9308516 5.0 4.829837 1.4826 1 9 8 0.0261056 -0.4419437 0.0834780
valence_52 52 536 8.029851 1.2601160 8.0 8.260465 1.4826 1 9 8 -1.7623263 4.0879337 0.0544287
valence_53 53 536 2.130597 1.2506882 2.0 1.953488 1.4826 1 9 8 1.3607913 2.7849185 0.0540215
valence_54 54 536 3.044776 1.3429813 3.0 2.983721 1.4826 1 9 8 0.5748561 0.6778795 0.0580080
valence_55 55 536 4.772388 1.2214832 5.0 4.811628 0.0000 1 9 8 -0.3159328 2.0045545 0.0527600
valence_56 56 535 5.074766 1.2697941 5.0 5.097902 0.0000 1 9 8 -0.3098065 2.0131102 0.0548980
valence_57 57 536 5.481343 1.4887826 5.0 5.453488 1.4826 1 9 8 -0.0724402 0.7411982 0.0643056
valence_58 58 536 5.722015 1.5184721 6.0 5.753488 1.4826 1 9 8 -0.3659351 0.7212373 0.0655880
valence_59 59 535 6.485981 1.6802693 7.0 6.578089 1.4826 1 9 8 -0.5703198 0.3545438 0.0726444
valence_60 60 536 4.880597 1.5433658 5.0 4.960465 1.4826 1 9 8 -0.5067813 0.6854062 0.0666632
valence_61 61 536 2.192164 1.4346885 2.0 1.967442 1.4826 1 9 8 1.4166661 2.3074799 0.0619691
valence_62 62 536 5.569030 1.4217574 5.0 5.534884 1.4826 1 9 8 0.0026730 0.4739881 0.0614106
valence_63 63 536 4.746269 0.9042915 5.0 4.846512 0.0000 1 9 8 -0.9037931 6.0652404 0.0390594
valence_64 64 536 1.682836 1.1208159 1.0 1.451163 0.0000 1 9 8 2.5210170 8.7662274 0.0484119
valence_65 65 536 5.671642 1.7327030 6.0 5.686046 1.4826 1 9 8 -0.1172004 0.1737979 0.0748414
valence_66 66 536 2.626866 1.5208047 2.0 2.446512 1.4826 1 9 8 0.9520743 0.6938394 0.0656888
valence_67 67 536 4.192164 1.4281595 4.0 4.200000 1.4826 1 9 8 0.0718130 0.9511162 0.0616871
valence_68 68 536 4.863806 1.2656924 5.0 4.916279 0.0000 1 9 8 -0.3896465 2.0428691 0.0546696
valence_69 69 534 2.953184 1.4193976 3.0 2.869159 1.4826 1 9 8 0.5775011 0.4777612 0.0614233
valence_70 70 536 3.436567 1.8794726 3.0 3.288372 1.4826 1 9 8 0.5547969 -0.2136271 0.0811809
valence_71 71 536 5.257463 1.0647014 5.0 5.225581 0.0000 1 9 8 -0.0333550 3.8304950 0.0459881
valence_72 72 536 4.897388 0.8832357 5.0 4.962791 0.0000 1 9 8 -0.6930263 9.9970766 0.0381500
valence_73 73 536 3.882463 1.7302126 4.0 3.865116 1.4826 1 9 8 0.1421369 -0.4706922 0.0747338
valence_74 74 536 4.022388 1.4311170 4.0 4.079070 1.4826 1 9 8 -0.2110689 0.6428369 0.0618148
valence_75 75 536 5.593284 1.2994145 5.0 5.581395 1.4826 1 9 8 -0.1001140 1.5371138 0.0561262
valence_76 76 534 6.140449 1.4742736 6.0 6.135514 1.4826 1 9 8 -0.1195706 0.5322742 0.0637981
valence_77 77 536 6.266791 1.6569586 6.0 6.323256 1.4826 1 9 8 -0.5088666 0.5475596 0.0715697
valence_78 78 536 5.623134 1.5321313 5.0 5.616279 1.4826 1 9 8 -0.1376852 0.6873413 0.0661780
valence_79 79 536 5.591418 1.7590995 6.0 5.623256 1.4826 1 9 8 -0.1381898 -0.1099905 0.0759815
valence_80 80 536 5.919776 1.3308351 6.0 5.897674 1.4826 1 9 8 -0.1192622 1.0407881 0.0574833
valence_81 81 535 1.484112 1.0071049 1.0 1.233100 0.0000 1 9 8 3.0736322 12.1014880 0.0435409
valence_82 82 536 5.919776 1.6886373 6.0 5.965116 1.4826 1 9 8 -0.3183236 0.4404850 0.0729380
valence_83 83 536 4.945895 1.6896778 5.0 4.972093 1.4826 1 9 8 -0.1055782 0.5273851 0.0729830
valence_84 84 536 3.916045 1.6355982 4.0 3.913953 1.4826 1 9 8 0.1934920 0.4293696 0.0706471
valence_85 85 536 5.208955 1.7723721 5.0 5.218605 1.4826 1 9 8 -0.1120560 -0.0357700 0.0765548
valence_86 86 536 5.117537 1.2660891 5.0 5.132558 0.0000 1 9 8 -0.3146368 2.1280314 0.0546867
valence_87 87 536 4.218284 1.8733053 4.0 4.186046 1.4826 1 9 8 0.1628912 -0.5094232 0.0809145
valence_88 88 534 4.970038 1.8476295 5.0 5.007009 1.4826 1 9 8 -0.1040446 -0.3575186 0.0799547
valence_89 89 533 5.442777 1.9201664 5.0 5.473068 1.4826 1 9 8 -0.1011778 -0.3310685 0.0831716
valence_90 90 536 5.052239 1.1442970 5.0 5.041861 0.0000 1 9 8 0.0773705 4.9972467 0.0494261
valence_91 91 536 6.309702 1.5079785 6.0 6.313954 1.4826 1 9 8 -0.2721737 0.4400108 0.0651348
valence_92 92 536 5.658582 1.4715498 6.0 5.676744 1.4826 1 9 8 -0.3027209 1.0189623 0.0635613
valence_93 93 536 5.861940 1.5801240 6.0 5.865116 1.4826 1 9 8 -0.1812764 0.7054291 0.0682510
valence_94 94 536 4.091418 2.0336058 4.0 4.009302 1.4826 1 9 8 0.3646561 -0.5227787 0.0878384
valence_95 95 536 3.583955 1.5673193 4.0 3.544186 1.4826 1 9 8 0.2693887 -0.2423997 0.0676979
valence_96 96 536 4.619403 1.7796136 5.0 4.637209 1.4826 1 9 8 -0.0787327 0.0553815 0.0768676
valence_97 97 536 4.292910 1.8620725 4.0 4.216279 1.4826 1 9 8 0.3298455 -0.3574011 0.0804293
valence_98 98 536 5.210821 1.7321464 5.0 5.202326 1.4826 1 9 8 -0.0435228 0.2418465 0.0748173
valence_99 99 536 4.611940 1.6367757 4.0 4.567442 1.4826 1 9 8 0.2799251 0.0111992 0.0706979
valence_100 100 536 6.968284 1.4197939 7.0 7.034884 1.4826 1 9 8 -0.5621498 0.4834454 0.0613258
arousal_1 101 541 5.621072 2.3794334 6.0 5.810624 1.4826 1 9 8 -0.6808376 -0.6105638 0.1022998
arousal_2 102 541 4.031423 2.0378210 4.0 3.963049 2.9652 1 9 8 0.1999880 -0.8092622 0.0876128
arousal_3 103 541 5.334566 2.1432252 6.0 5.418014 1.4826 1 9 8 -0.3843177 -0.6266262 0.0921444
arousal_4 104 540 4.100000 2.0497522 4.0 4.057870 2.9652 1 9 8 0.0537751 -0.8983071 0.0882073
arousal_5 105 540 4.812963 2.2047803 5.0 4.870370 2.9652 1 9 8 -0.2811748 -0.9192271 0.0948786
arousal_6 106 541 2.704251 1.8879149 2.0 2.542725 1.4826 1 9 8 0.5806180 -1.1244280 0.0811678
arousal_7 107 540 3.124074 2.0197021 3.0 2.949074 2.9652 1 9 8 0.4579905 -0.9651087 0.0869141
arousal_8 108 541 2.907579 1.9414480 2.0 2.745958 1.4826 1 9 8 0.5089204 -1.0618684 0.0834694
arousal_9 109 541 2.730129 1.8917946 2.0 2.577367 1.4826 1 9 8 0.5687614 -1.0948468 0.0813346
arousal_10 110 540 4.951852 1.9919821 5.0 4.976852 1.4826 1 9 8 -0.1797011 -0.4841428 0.0857213
arousal_11 111 541 4.253235 2.1258144 5.0 4.244804 2.9652 1 9 8 -0.0399629 -1.0250309 0.0913959
arousal_12 112 540 3.970370 2.0789277 4.0 3.914352 2.9652 1 9 8 0.0641157 -0.9630114 0.0894628
arousal_13 113 540 5.955556 2.2885264 6.0 6.173611 1.4826 1 9 8 -0.7613257 -0.2167046 0.0984825
arousal_14 114 541 5.894640 2.3384842 6.0 6.094688 2.9652 1 9 8 -0.6745831 -0.4245017 0.1005393
arousal_15 115 540 5.338889 2.0353934 6.0 5.442130 1.4826 1 9 8 -0.4814427 -0.3456202 0.0875894
arousal_16 116 541 5.044362 2.1059595 5.0 5.096998 1.4826 1 9 8 -0.3112584 -0.5263692 0.0905423
arousal_17 117 539 6.335807 2.5825476 7.0 6.662818 1.4826 1 9 8 -0.9840896 -0.2408397 0.1112382
arousal_18 118 541 5.896488 2.1705961 6.0 6.064665 1.4826 1 9 8 -0.6371854 -0.2488783 0.0933212
arousal_19 119 540 4.827778 2.1123294 5.0 4.842593 2.9652 1 9 8 -0.1520111 -0.8361072 0.0909002
arousal_20 120 541 2.787431 1.9638183 2.0 2.558892 1.4826 1 9 8 0.8025363 -0.3715981 0.0844311
arousal_21 121 541 5.255083 2.0352443 6.0 5.330254 1.4826 1 9 8 -0.3667483 -0.5184239 0.0875020
arousal_22 122 541 4.205176 2.0038045 5.0 4.203233 1.4826 1 9 8 -0.0960942 -0.9906488 0.0861503
arousal_23 123 541 4.504621 2.3637848 5.0 4.464203 2.9652 1 9 8 -0.0330572 -1.1189557 0.1016270
arousal_24 124 540 4.890741 2.2325612 5.0 4.953704 2.9652 1 9 8 -0.3464295 -0.8741986 0.0960741
arousal_25 125 541 5.713494 2.5322335 6.0 5.891455 2.9652 1 9 8 -0.6732572 -0.7422981 0.1088692
arousal_26 126 540 3.885185 1.8838644 4.0 3.863426 1.4826 1 9 8 0.0326009 -0.8617841 0.0810686
arousal_27 127 539 4.083488 2.0985227 4.0 4.011547 2.9652 1 9 8 0.1500209 -0.7996554 0.0903898
arousal_28 128 540 5.775926 2.3018453 6.0 5.942130 1.4826 1 9 8 -0.6196675 -0.5140196 0.0990556
arousal_29 129 540 6.162963 2.6234490 7.0 6.453704 1.4826 1 9 8 -0.8910572 -0.4821664 0.1128953
arousal_30 130 539 3.487941 2.0777953 3.0 3.334873 2.9652 1 9 8 0.4228341 -0.7296508 0.0894970
arousal_31 131 540 2.737037 1.8945578 2.0 2.546296 1.4826 1 9 8 0.7031490 -0.6894424 0.0815288
arousal_32 132 539 2.764378 1.8367763 2.0 2.598152 1.4826 1 9 8 0.6410206 -0.8359133 0.0791156
arousal_33 133 540 5.537037 2.2621592 6.0 5.678241 1.4826 1 9 8 -0.5386969 -0.4719313 0.0973478
arousal_34 134 540 4.135185 2.1857743 4.0 4.074074 2.9652 1 9 8 0.1062771 -0.9682949 0.0940608
arousal_35 135 539 4.504638 2.1551091 5.0 4.498845 2.9652 1 9 8 -0.0497018 -0.8308722 0.0928271
arousal_36 136 540 4.062963 2.0512267 5.0 4.032407 1.4826 1 9 8 -0.0447392 -1.0065924 0.0882707
arousal_37 137 540 5.779630 2.1548478 6.0 5.969907 1.4826 1 9 8 -0.7119252 -0.1416199 0.0927299
arousal_38 138 540 6.501852 2.7623892 8.0 6.877315 1.4826 1 9 8 -1.0633254 -0.2861846 0.1188743
arousal_39 139 540 5.407407 2.4022139 6.0 5.523148 2.9652 1 9 8 -0.4176104 -0.8539825 0.1033748
arousal_40 140 540 3.983333 1.9723741 4.0 3.939815 1.4826 1 9 8 0.1100159 -0.7196616 0.0848775
arousal_41 141 540 6.001852 2.2335767 6.0 6.210648 1.4826 1 9 8 -0.7092867 -0.2073858 0.0961178
arousal_42 142 540 5.161111 2.1652438 6.0 5.289352 1.4826 1 9 8 -0.5543246 -0.5420969 0.0931773
arousal_43 143 539 5.866419 2.1985050 6.0 6.046189 1.4826 1 9 8 -0.7271191 -0.1288243 0.0946963
arousal_44 144 540 2.740741 1.8584929 2.0 2.567130 1.4826 1 8 7 0.6604586 -0.8406688 0.0799768
arousal_45 145 539 5.482375 2.2196246 6.0 5.660508 1.4826 1 9 8 -0.6464319 -0.4297823 0.0956060
arousal_46 146 540 5.333333 2.2323312 6.0 5.469907 1.4826 1 9 8 -0.4854203 -0.6285827 0.0960642
arousal_47 147 540 6.057407 2.1908989 6.0 6.293982 1.4826 1 9 8 -0.8590956 0.1199776 0.0942813
arousal_48 148 539 4.051948 1.8933190 4.0 4.048499 1.4826 1 9 8 -0.0121997 -0.6973739 0.0815510
arousal_49 149 539 4.452690 2.1030795 5.0 4.457275 1.4826 1 9 8 -0.1271704 -0.7716484 0.0905860
arousal_50 150 540 6.420370 2.5928315 7.0 6.775463 1.4826 1 9 8 -1.0106337 -0.1834490 0.1115777
arousal_51 151 534 5.310861 1.9746978 6.0 5.422897 1.4826 1 9 8 -0.5458105 -0.2375294 0.0854535
arousal_52 152 536 6.729478 2.0651873 7.0 7.039535 1.4826 1 9 8 -1.1523959 0.9090466 0.0892025
arousal_53 153 536 6.406716 2.6131715 7.0 6.753488 2.2239 1 9 8 -0.9651883 -0.3113564 0.1128718
arousal_54 154 536 4.843284 2.1758384 5.0 4.909302 2.9652 1 9 8 -0.3088246 -0.8254924 0.0939819
arousal_55 155 536 3.505597 1.9527040 3.0 3.413953 2.9652 1 9 8 0.2524466 -0.8443040 0.0843440
arousal_56 156 536 3.645522 1.9644738 4.0 3.567442 2.9652 1 9 8 0.1937946 -0.8604313 0.0848523
arousal_57 157 535 4.041122 1.8365287 4.0 4.041958 1.4826 1 9 8 -0.0006724 -0.6464555 0.0794001
arousal_58 158 536 4.354478 1.9843543 5.0 4.369767 1.4826 1 9 8 -0.0425377 -0.7342813 0.0857111
arousal_59 159 536 4.889925 2.1428017 5.0 4.918605 2.9652 1 9 8 -0.1860362 -0.8131370 0.0925549
arousal_60 160 535 4.472897 1.9417341 5.0 4.505827 1.4826 1 9 8 -0.1352945 -0.6191260 0.0839485
arousal_61 161 536 6.147388 2.4946134 7.0 6.430233 1.4826 1 9 8 -0.8591341 -0.3763184 0.1077509
arousal_62 162 536 4.837687 1.8183276 5.0 4.946512 1.4826 1 9 8 -0.5093604 -0.3018838 0.0785398
arousal_63 163 536 2.707090 1.9155112 2.0 2.551163 1.4826 1 9 8 0.5710953 -1.1475852 0.0827375
arousal_64 164 535 6.708411 2.6712901 8.0 7.130536 1.4826 1 9 8 -1.2327293 0.1540149 0.1154899
arousal_65 165 536 4.750000 2.1825562 5.0 4.739535 2.9652 1 9 8 -0.0714652 -0.8706885 0.0942721
arousal_66 166 535 5.474766 2.3833887 6.0 5.620047 2.9652 1 9 8 -0.5113302 -0.7806575 0.1030429
arousal_67 167 536 3.636194 1.9962853 4.0 3.560465 2.9652 1 9 8 0.1697349 -1.0624011 0.0862264
arousal_68 168 536 3.501866 2.0213802 3.0 3.400000 2.9652 1 9 8 0.2385400 -1.0211984 0.0873103
arousal_69 169 536 5.511194 2.3120703 6.0 5.686046 1.4826 1 9 8 -0.6568640 -0.5863677 0.0998662
arousal_70 170 536 5.638060 2.2213003 6.0 5.827907 1.4826 1 9 8 -0.6846725 -0.3213796 0.0959456
arousal_71 171 535 3.280374 1.9905883 3.0 3.128205 2.9652 1 9 8 0.4328206 -0.7690077 0.0860606
arousal_72 172 535 2.970093 1.8710897 2.0 2.864802 1.4826 1 9 8 0.4593250 -0.8698084 0.0808943
arousal_73 173 536 5.639925 2.1406199 6.0 5.806977 1.4826 1 9 8 -0.6683344 -0.1705345 0.0924607
arousal_74 174 536 4.192164 2.0685482 5.0 4.167442 1.4826 1 9 8 -0.0384711 -0.9325219 0.0893477
arousal_75 175 536 3.576493 1.9232293 4.0 3.497674 1.4826 1 9 8 0.2029690 -0.8100441 0.0830709
arousal_76 176 535 4.347664 2.0578408 5.0 4.305361 2.9652 1 9 8 0.1456330 -0.6650043 0.0889682
arousal_77 177 536 5.139925 2.0618953 5.0 5.200000 1.4826 1 9 8 -0.3127026 -0.3732340 0.0890603
arousal_78 178 536 4.602612 2.0113493 5.0 4.658139 1.4826 1 9 8 -0.2185290 -0.8056102 0.0868771
arousal_79 179 536 5.970149 2.0340661 6.0 6.139535 1.4826 1 9 8 -0.6991472 -0.0100929 0.0878583
arousal_80 180 536 4.289179 1.9784716 5.0 4.286047 1.4826 1 9 8 -0.0854583 -0.7548291 0.0854570
arousal_81 181 536 6.425373 2.7530832 7.5 6.776744 2.2239 1 9 8 -1.0247909 -0.3523735 0.1189151
arousal_82 182 536 4.718284 1.9786056 5.0 4.765116 1.4826 1 9 8 -0.1823001 -0.5932278 0.0854627
arousal_83 183 536 3.988806 2.2778629 4.0 3.855814 2.9652 1 9 8 0.2524418 -0.8751976 0.0983887
arousal_84 184 535 4.641121 2.0812590 5.0 4.675991 1.4826 1 9 8 -0.1216858 -0.6765756 0.0899807
arousal_85 185 535 4.861682 2.0036318 5.0 4.960373 1.4826 1 9 8 -0.3957426 -0.5721818 0.0866246
arousal_86 186 536 4.082090 1.8056961 5.0 4.111628 1.4826 1 9 8 -0.1508005 -0.6165233 0.0779942
arousal_87 187 536 5.330224 2.0582864 6.0 5.462791 1.4826 1 9 8 -0.5930146 -0.3746321 0.0889044
arousal_88 188 536 5.722015 1.9904352 6.0 5.906977 1.4826 1 9 8 -0.7371702 0.0783366 0.0859737
arousal_89 189 536 6.020522 2.2005837 6.0 6.225581 1.4826 1 9 8 -0.7171924 -0.1689512 0.0950507
arousal_90 190 535 3.009346 1.9025674 2.0 2.890443 1.4826 1 9 8 0.5028700 -0.6920294 0.0822552
arousal_91 191 536 4.908582 1.9491290 5.0 4.983721 1.4826 1 9 8 -0.3253081 -0.4190262 0.0841895
arousal_92 192 536 4.897388 1.8175450 5.0 5.025581 1.4826 1 9 8 -0.5600445 -0.0729064 0.0785060
arousal_93 193 536 4.283582 2.0456909 5.0 4.248837 1.4826 1 9 8 0.0834765 -0.6252435 0.0883604
arousal_94 194 536 5.662313 2.3473504 6.0 5.860465 1.4826 1 9 8 -0.6923747 -0.5232144 0.1013901
arousal_95 195 536 5.617537 2.3236272 6.0 5.795349 1.4826 1 9 8 -0.6127793 -0.6056714 0.1003654
arousal_96 196 536 4.891791 2.0921410 5.0 4.967442 1.4826 1 9 8 -0.3457631 -0.6386402 0.0903667
arousal_97 197 536 5.343284 1.9806710 6.0 5.483721 1.4826 1 9 8 -0.6327133 -0.2107756 0.0855520
arousal_98 198 535 5.801869 2.0558668 6.0 5.946387 1.4826 1 9 8 -0.5941985 -0.0869923 0.0888829
arousal_99 199 536 5.675373 1.9866446 6.0 5.881395 1.4826 1 9 8 -0.8155053 0.1591686 0.0858100
arousal_100 200 535 4.585047 2.2518741 5.0 4.552448 2.9652 1 9 8 0.0306351 -0.9774071 0.0973570 
#Stats
image_ratings_all %>%
  select(valence_human, valence_ml, adjusted_valence) %>%
  tbl_summary(
    label = list(valence_human ~"Human", valence_ml ~ "Machine", adjusted_valence ~ "OASIS Adjusted"),
    statistic = list(all_continuous() ~ "{min}-{max} ({mean}, {SD})"),
    missing = "ifany") %>%
  modify_header(label ~ "") %>%
  as_gt() %>%
  tab_header("Valence")
Valence
N = 1001
Human 1.48-8.03 (4.75, 1.31)
Machine 3.66-7.15 (5.14, 0.95)
OASIS Adjusted 2.01-8.05 (4.73, 2.08)
    Unknown 88
1 Minimum-Maximum (Mean, SD) 
image_ratings_all %>%
  select(arousal_human, arousal_ml, adjusted_arousal) %>%
  tbl_summary(label = list(arousal_human ~"Human", arousal_ml ~ "Machine", 
                           adjusted_arousal ~ "OASIS Adjusted"),
    statistic = list(all_continuous() ~ "{min}-{max} ({mean}, {SD})"),
    missing = "ifany") %>%
  modify_header(label ~ "") %>%
  as_gt() %>%
  tab_header("Arousal")
Arousal
N = 1001
Human 2.70-6.73 (4.77, 1.06)
Machine 3.86-6.24 (4.85, 0.72)
OASIS Adjusted 3.87-6.33 (4.97, 0.82)
    Unknown 88
1 Minimum-Maximum (Mean, SD) 
#Stats by Source
image_ratings_all %>%
  select(source, valence_human, valence_ml) %>%
  tbl_summary(by = source, 
              label = list(valence_human ~"Human", valence_ml ~ "Machine"),
              statistic = list(all_continuous() ~ "{min}-{max} ({mean}, {SD})"),
              missing = "ifany") %>%
  modify_header(label ~ "") %>%
  as_gt() %>%
  tab_header("Valence")
Valence
OASIS, N = 121 smartphone, N = 881
Human 2.00-8.03 (4.68, 1.98) 1.48-7.38 (4.76, 1.20)
Machine 3.78-7.15 (5.38, 1.24) 3.66-6.99 (5.11, 0.91)
1 Minimum-Maximum (Mean, SD) 
image_ratings_all %>%
  select(source, arousal_human, arousal_ml) %>%
  tbl_summary(by = source, 
              label = list(arousal_human ~"Human", arousal_ml ~ "Machine"),
              statistic = list(all_continuous() ~ "{min}-{max} ({mean}, {SD})"),
              missing = "ifany") %>%
  modify_header(label ~ "") %>%
  as_gt() %>%
  tab_header("Arousal")
Arousal
OASIS, N = 121 smartphone, N = 881
Human 4.03-6.73 (5.32, 0.89) 2.70-6.71 (4.70, 1.07)
Machine 4.13-6.24 (5.18, 0.64) 3.86-6.03 (4.80, 0.73)
1 Minimum-Maximum (Mean, SD)

Correlation Tests

Let’s look at the correlation between the machine and human ratings for the 100 images.

# All Images
cor.test(image_ratings_all$valence_human, image_ratings_all$valence_ml)
## 
##  Pearson's product-moment correlation
## 
## data:  image_ratings_all$valence_human and image_ratings_all$valence_ml
## t = 4.4837, df = 98, p-value = 1.995e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2352184 0.5633416
## sample estimates:
##       cor 
## 0.4125751
cor.test(image_ratings_all$arousal_human, image_ratings_all$arousal_ml)
## 
##  Pearson's product-moment correlation
## 
## data:  image_ratings_all$arousal_human and image_ratings_all$arousal_ml
## t = 5.5485, df = 98, p-value = 2.453e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3235776 0.6252911
## sample estimates:
##       cor 
## 0.4889216
# Smartphone Only
cor.test(image_ratings_smartphone$valence_human, image_ratings_smartphone$valence_ml)
## 
##  Pearson's product-moment correlation
## 
## data:  image_ratings_smartphone$valence_human and image_ratings_smartphone$valence_ml
## t = 2.6429, df = 86, p-value = 0.009766
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.0685738 0.4572727
## sample estimates:
##       cor 
## 0.2740797
cor.test(image_ratings_smartphone$arousal_human, image_ratings_smartphone$arousal_ml)
## 
##  Pearson's product-moment correlation
## 
## data:  image_ratings_smartphone$arousal_human and image_ratings_smartphone$arousal_ml
## t = 4.999, df = 86, p-value = 3.004e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2943159 0.6221228
## sample estimates:
##       cor 
## 0.4745084
cor.test(image_ratings_smartphone$arousal_human,image_ratings_smartphone$valence_human)
## 
##  Pearson's product-moment correlation
## 
## data:  image_ratings_smartphone$arousal_human and image_ratings_smartphone$valence_human
## t = -3.2914, df = 86, p-value = 0.001447
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.5083133 -0.1344577
## sample estimates:
##        cor 
## -0.3344806
cor.test(image_ratings_smartphone$arousal_ml, image_ratings_smartphone$valence_ml)
## 
##  Pearson's product-moment correlation
## 
## data:  image_ratings_smartphone$arousal_ml and image_ratings_smartphone$valence_ml
## t = 4.5787, df = 86, p-value = 1.567e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2571107 0.5968167
## sample estimates:
##       cor 
## 0.4427129
# OASIS Only
cor.test(image_ratings_OASIS$valence_human, image_ratings_OASIS$valence_ml)
## 
##  Pearson's product-moment correlation
## 
## data:  image_ratings_OASIS$valence_human and image_ratings_OASIS$valence_ml
## t = 7.3693, df = 10, p-value = 2.399e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.7301304 0.9773935
## sample estimates:
##       cor 
## 0.9189647
cor.test(image_ratings_OASIS$arousal_human, image_ratings_OASIS$arousal_ml)
## 
##  Pearson's product-moment correlation
## 
## data:  image_ratings_OASIS$arousal_human and image_ratings_OASIS$arousal_ml
## t = 1.5694, df = 10, p-value = 0.1476
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1736468  0.8114358
## sample estimates:
##       cor 
## 0.4445578
cor.test(image_ratings_OASIS$arousal_human, image_ratings_OASIS$valence_human)
## 
##  Pearson's product-moment correlation
## 
## data:  image_ratings_OASIS$arousal_human and image_ratings_OASIS$valence_human
## t = -0.3572, df = 10, p-value = 0.7284
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.6446201  0.4934450
## sample estimates:
##        cor 
## -0.1122424
cor.test(image_ratings_OASIS$arousal_ml, image_ratings_OASIS$valence_ml)
## 
##  Pearson's product-moment correlation
## 
## data:  image_ratings_OASIS$arousal_ml and image_ratings_OASIS$valence_ml
## t = -0.67258, df = 10, p-value = 0.5165
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.6985377  0.4154695
## sample estimates:
##       cor 
## -0.208036
#Adjusted Arousal vs Machine Arousal
cor.test(image_ratings_OASIS$adjusted_arousal, image_ratings_OASIS$arousal_ml)
## 
##  Pearson's product-moment correlation
## 
## data:  image_ratings_OASIS$adjusted_arousal and image_ratings_OASIS$arousal_ml
## t = 1.2951, df = 10, p-value = 0.2244
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.2490919  0.7826595
## sample estimates:
##       cor 
## 0.3789876
#Adjusted Valence vs Machine Valence
cor.test(image_ratings_OASIS$adjusted_valence, image_ratings_OASIS$valence_ml)
## 
##  Pearson's product-moment correlation
## 
## data:  image_ratings_OASIS$adjusted_valence and image_ratings_OASIS$valence_ml
## t = 9.1492, df = 10, p-value = 3.569e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.811295 0.984844
## sample estimates:
##       cor 
## 0.9451373
#Adjusted Arousal vs Human 
cor.test(image_ratings_OASIS$adjusted_arousal, image_ratings_OASIS$arousal_human)
## 
##  Pearson's product-moment correlation
## 
## data:  image_ratings_OASIS$adjusted_arousal and image_ratings_OASIS$arousal_human
## t = 5.3868, df = 10, p-value = 0.0003071
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.5711619 0.9607748
## sample estimates:
##       cor 
## 0.8623825
#Adjusted Valence vs Human
cor.test(image_ratings_OASIS$adjusted_valence, image_ratings_OASIS$valence_human)
## 
##  Pearson's product-moment correlation
## 
## data:  image_ratings_OASIS$adjusted_valence and image_ratings_OASIS$valence_human
## t = 15.806, df = 10, p-value = 2.113e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.9300490 0.9947013
## sample estimates:
##       cor 
## 0.9805667

ICC

#All Images Valence ICC
psych::ICC(image_ratings_all[,c("valence_human","valence_ml")])
## Call: psych::ICC(x = image_ratings_all[, c("valence_human", "valence_ml")])
## 
## Intraclass correlation coefficients 
##                          type  ICC   F df1 df2       p lower bound upper bound
## Single_raters_absolute   ICC1 0.36 2.1  99 100 1.2e-04        0.17        0.52
## Single_random_raters     ICC2 0.37 2.3  99  99 2.4e-05        0.19        0.53
## Single_fixed_raters      ICC3 0.39 2.3  99  99 2.4e-05        0.21        0.55
## Average_raters_absolute ICC1k 0.53 2.1  99 100 1.2e-04        0.30        0.68
## Average_random_raters   ICC2k 0.54 2.3  99  99 2.4e-05        0.32        0.69
## Average_fixed_raters    ICC3k 0.56 2.3  99  99 2.4e-05        0.35        0.71
## 
##  Number of subjects = 100     Number of Judges =  2
## See the help file for a discussion of the other 4 McGraw and Wong estimates,
#All Images Arousal ICC
psych::ICC(image_ratings_all[,c("arousal_human","arousal_ml")])
## Call: psych::ICC(x = image_ratings_all[, c("arousal_human", "arousal_ml")])
## 
## Intraclass correlation coefficients 
##                          type  ICC   F df1 df2       p lower bound upper bound
## Single_raters_absolute   ICC1 0.46 2.7  99 100 7.6e-07        0.29        0.60
## Single_random_raters     ICC2 0.46 2.7  99  99 8.2e-07        0.29        0.60
## Single_fixed_raters      ICC3 0.46 2.7  99  99 8.2e-07        0.29        0.60
## Average_raters_absolute ICC1k 0.63 2.7  99 100 7.6e-07        0.45        0.75
## Average_random_raters   ICC2k 0.63 2.7  99  99 8.2e-07        0.45        0.75
## Average_fixed_raters    ICC3k 0.63 2.7  99  99 8.2e-07        0.44        0.75
## 
##  Number of subjects = 100     Number of Judges =  2
## See the help file for a discussion of the other 4 McGraw and Wong estimates,
#Smartphone Valence ICC
psych::ICC(image_ratings_smartphone[,c("valence_human","valence_ml")])
## Call: psych::ICC(x = image_ratings_smartphone[, c("valence_human", 
##     "valence_ml")])
## 
## Intraclass correlation coefficients 
##                          type  ICC   F df1 df2      p lower bound upper bound
## Single_raters_absolute   ICC1 0.23 1.6  87  88 0.0132       0.028        0.42
## Single_random_raters     ICC2 0.25 1.7  87  87 0.0063       0.054        0.43
## Single_fixed_raters      ICC3 0.26 1.7  87  87 0.0063       0.058        0.45
## Average_raters_absolute ICC1k 0.38 1.6  87  88 0.0132       0.055        0.59
## Average_random_raters   ICC2k 0.40 1.7  87  87 0.0063       0.103        0.60
## Average_fixed_raters    ICC3k 0.42 1.7  87  87 0.0063       0.111        0.62
## 
##  Number of subjects = 88     Number of Judges =  2
## See the help file for a discussion of the other 4 McGraw and Wong estimates,
#Smartphone Arousal ICC
psych::ICC(image_ratings_smartphone[,c("arousal_human","arousal_ml")])
## Call: psych::ICC(x = image_ratings_smartphone[, c("arousal_human", 
##     "arousal_ml")])
## 
## Intraclass correlation coefficients 
##                          type  ICC   F df1 df2       p lower bound upper bound
## Single_raters_absolute   ICC1 0.44 2.6  87  88 7.1e-06        0.26        0.59
## Single_random_raters     ICC2 0.44 2.6  87  87 7.6e-06        0.26        0.59
## Single_fixed_raters      ICC3 0.44 2.6  87  87 7.6e-06        0.26        0.59
## Average_raters_absolute ICC1k 0.61 2.6  87  88 7.1e-06        0.41        0.75
## Average_random_raters   ICC2k 0.61 2.6  87  87 7.6e-06        0.41        0.75
## Average_fixed_raters    ICC3k 0.61 2.6  87  87 7.6e-06        0.41        0.75
## 
##  Number of subjects = 88     Number of Judges =  2
## See the help file for a discussion of the other 4 McGraw and Wong estimates,
#OASIS Valence ICC
psych::ICC(image_ratings_OASIS[,c("valence_human","valence_ml")])
## Call: psych::ICC(x = image_ratings_OASIS[, c("valence_human", "valence_ml")])
## 
## Intraclass correlation coefficients 
##                          type  ICC    F df1 df2       p lower bound upper bound
## Single_raters_absolute   ICC1 0.76  7.4  11  12 0.00082        0.38        0.92
## Single_random_raters     ICC2 0.77 10.6  11  11 0.00024        0.30        0.93
## Single_fixed_raters      ICC3 0.83 10.6  11  11 0.00024        0.51        0.95
## Average_raters_absolute ICC1k 0.87  7.4  11  12 0.00082        0.55        0.96
## Average_random_raters   ICC2k 0.87 10.6  11  11 0.00024        0.46        0.96
## Average_fixed_raters    ICC3k 0.91 10.6  11  11 0.00024        0.67        0.97
## 
##  Number of subjects = 12     Number of Judges =  2
## See the help file for a discussion of the other 4 McGraw and Wong estimates,
#OASIS Arousal ICC
psych::ICC(image_ratings_OASIS[,c("arousal_human","arousal_ml")])
## Call: psych::ICC(x = image_ratings_OASIS[, c("arousal_human", "arousal_ml")])
## 
## Intraclass correlation coefficients 
##                          type  ICC   F df1 df2     p lower bound upper bound
## Single_raters_absolute   ICC1 0.44 2.6  11  12 0.058       -0.12        0.80
## Single_random_raters     ICC2 0.44 2.6  11  11 0.065       -0.13        0.80
## Single_fixed_raters      ICC3 0.44 2.6  11  11 0.065       -0.15        0.80
## Average_raters_absolute ICC1k 0.61 2.6  11  12 0.058       -0.28        0.89
## Average_random_raters   ICC2k 0.61 2.6  11  11 0.065       -0.29        0.89
## Average_fixed_raters    ICC3k 0.61 2.6  11  11 0.065       -0.34        0.89
## 
##  Number of subjects = 12     Number of Judges =  2
## See the help file for a discussion of the other 4 McGraw and Wong estimates,
#OASIS Human Valence ICC
psych::ICC(image_ratings_OASIS[,c("valence_human", "adjusted_valence")])
## Call: psych::ICC(x = image_ratings_OASIS[, c("valence_human", "adjusted_valence")])
## 
## Intraclass correlation coefficients 
##                          type  ICC   F df1 df2       p lower bound upper bound
## Single_raters_absolute   ICC1 0.98 104  11  12 3.9e-10        0.94        0.99
## Single_random_raters     ICC2 0.98 104  11  11 1.8e-09        0.94        0.99
## Single_fixed_raters      ICC3 0.98 104  11  11 1.8e-09        0.94        0.99
## Average_raters_absolute ICC1k 0.99 104  11  12 3.9e-10        0.97        1.00
## Average_random_raters   ICC2k 0.99 104  11  11 1.8e-09        0.97        1.00
## Average_fixed_raters    ICC3k 0.99 104  11  11 1.8e-09        0.97        1.00
## 
##  Number of subjects = 12     Number of Judges =  2
## See the help file for a discussion of the other 4 McGraw and Wong estimates,
#OASIS Human Arousal ICC
psych::ICC(image_ratings_OASIS[,c("arousal_human", "adjusted_arousal")])
## Call: psych::ICC(x = image_ratings_OASIS[, c("arousal_human", "adjusted_arousal")])
## 
## Intraclass correlation coefficients 
##                          type  ICC    F df1 df2       p lower bound upper bound
## Single_raters_absolute   ICC1 0.79  8.7  11  12 4.0e-04        0.45        0.93
## Single_random_raters     ICC2 0.80 13.2  11  11 8.4e-05        0.31        0.94
## Single_fixed_raters      ICC3 0.86 13.2  11  11 8.4e-05        0.58        0.96
## Average_raters_absolute ICC1k 0.88  8.7  11  12 4.0e-04        0.62        0.97
## Average_random_raters   ICC2k 0.89 13.2  11  11 8.4e-05        0.47        0.97
## Average_fixed_raters    ICC3k 0.92 13.2  11  11 8.4e-05        0.74        0.98
## 
##  Number of subjects = 12     Number of Judges =  2
## See the help file for a discussion of the other 4 McGraw and Wong estimates,

Lin’s Correspondance Coefficient

#All Images Valence ICC
CCC(image_ratings_all$valence_human, image_ratings_all$valence_ml, 
    ci = "z-transform", conf.level = 0.95, na.rm = FALSE)
## $rho.c
##         est    lwr.ci    upr.ci
## 1 0.3701988 0.2085245 0.5121785
## 
## $s.shift
## [1] 0.7280459
## 
## $l.shift
## [1] 0.3568649
## 
## $C.b
## [1] 0.8972882
## 
## $blalt
##         mean       delta
## 1   4.231094 -1.76348145
## 2   5.010819  1.91921314
## 3   7.012616 -0.08866542
## 4   5.574274  0.95496401
## 5   5.831826 -0.24036190
## 6   5.134150 -0.60533612
## 7   5.307320 -1.36510305
## 8   5.808079 -0.88048266
## 9   5.806914 -0.84488180
## 10  3.757827 -3.44541445
## 11  5.714039 -0.51865151
## 12  5.478971  1.79621748
## 13  4.453889 -1.65084590
## 14  4.964480 -1.28940281
## 15  5.761132  1.96017546
## 16  4.941599  0.25013567
## 17  3.590095 -0.35796719
## 18  3.764079 -0.51152227
## 19  4.223812 -1.94854841
## 20  5.030330 -0.43843789
## 21  5.410856  0.48939836
## 22  6.232377  2.29080218
## 23  2.989497 -1.74195608
## 24  7.183542  0.07447522
## 25  4.536822  1.74857781
## 26  4.127332  0.75274330
## 27  5.093996 -0.18428865
## 28  4.422665 -0.99690098
## 29  4.510587 -0.66561892
## 30  5.491943  1.12722606
## 31  4.444251  1.52554553
## 32  6.260963  1.37066660
## 33  2.902122 -2.75609593
## 34  4.285512 -2.00170746
## 35  5.989656 -0.56079414
## 36  6.122158 -0.97024226
## 37  5.574306 -1.17079387
## 38  4.781054 -0.91395993
## 39  5.285436 -1.24865029
## 40  5.699477 -1.34719750
## 41  4.150373 -0.74972640
## 42  4.967931 -1.26178696
## 43  3.826475  0.21742018
## 44  5.358619 -1.01668274
## 45  4.914971 -0.51142414
## 46  5.247406 -1.14666423
## 47  2.956800 -1.90989615
## 48  4.701994  0.27077858
## 49  7.414296  1.23110875
## 50  2.954445 -1.64769608
## 51  3.434404 -0.77925568
## 52  5.105919 -0.66706224
## 53  4.965517  0.21849936
## 54  4.583227  1.79623268
## 55  5.503869  0.43629192
## 56  5.782253  1.40745731
## 57  4.211114  1.04172842
## 58  5.713391 -1.66558799
## 59  3.668289 -2.95224882
## 60  6.281572 -1.42508415
## 61  4.939851 -0.38716564
## 62  2.708492 -2.05131168
## 63  5.624503  0.09427779
## 64  3.223345 -1.19295803
## 65  3.986705  0.41091848
## 66  4.901721 -0.07582983
## 67  3.350918 -0.79546798
## 68  5.040710 -0.59897954
## 69  4.025206 -1.17727724
## 70  5.332566 -0.15020731
## 71  4.344186  1.10640436
## 72  4.108419 -0.45191271
## 73  3.883077  0.27862266
## 74  5.640004 -0.09344142
## 75  5.474637  1.33162584
## 76  5.549609  1.43436504
## 77  5.284225  0.67781893
## 78  4.633546  1.91574441
## 79  4.952772 -0.77800321
## 80  6.120279 -0.40100658
## 81  3.179912 -3.39160025
## 82  5.712910  0.41373212
## 83  4.317287  1.25721682
## 84  4.909296 -1.98650322
## 85  5.495317 -0.57272278
## 86  6.039788 -1.84450219
## 87  5.024727 -1.61288602
## 88  5.487065 -1.03405455
## 89  6.071973 -1.25839227
## 90  4.183344  0.99375611
## 91  5.867570 -1.63066159
## 92  5.797201  1.02500019
## 93  5.754038 -0.19091091
## 94  5.536918  0.65004390
## 95  5.127951 -2.07306659
## 96  4.622624 -2.07733678
## 97  5.432084 -1.62536202
## 98  4.534335 -0.48284955
## 99  4.981384  0.45887290
## 100 4.779010 -0.33414040
#All Images Arousal ICC
CCC(image_ratings_all$arousal_human, image_ratings_all$arousal_ml, 
    ci = "z-transform", conf.level = 0.95, na.rm = FALSE)
## $rho.c
##        est    lwr.ci    upr.ci
## 1 0.453063 0.2993062 0.5838856
## 
## $s.shift
## [1] 0.6797606
## 
## $l.shift
## [1] 0.08618118
## 
## $C.b
## [1] 0.9266579
## 
## $blalt
##         mean        delta
## 1   5.084842  1.072459489
## 2   4.997177 -0.090649548
## 3   4.860327 -0.550560971
## 4   4.086234  0.334001750
## 5   4.656127 -1.371513330
## 6   5.894015  0.123080556
## 7   5.786730  0.215818956
## 8   5.539622 -0.401467111
## 9   5.413266 -0.737807708
## 10  6.038216  0.595181650
## 11  5.670027  0.452921985
## 12  5.274936 -0.894316222
## 13  4.530509 -0.998170710
## 14  3.325194 -1.075525816
## 15  5.258760 -0.007352821
## 16  4.129794  0.150764201
## 17  4.379955  0.249333072
## 18  4.703859  0.373764341
## 19  4.930257  1.566473230
## 20  4.215534 -0.660697815
## 21  4.266440 -0.365904359
## 22  4.890584  1.770684526
## 23  5.365189  1.595547963
## 24  4.732736  1.203658619
## 25  3.898402 -0.820923369
## 26  3.483254 -1.492433263
## 27  3.334166 -1.139574121
## 28  4.790888  1.492298037
## 29  4.052831  0.164708185
## 30  4.426297  0.156683419
## 31  4.289259 -0.452591437
## 32  5.679035  0.201188930
## 33  5.382810  2.238084152
## 34  4.912491  0.989832007
## 35  4.417338 -0.634675400
## 36  4.832669 -1.698671667
## 37  5.775212  0.453279352
## 38  5.516847 -0.711471889
## 39  5.747799  0.237240795
## 40  3.305791 -1.130100959
## 41  5.677584 -0.390419232
## 42  5.634107 -0.601547367
## 43  5.572210  0.970394207
## 44  4.876308 -1.648719948
## 45  4.425896  0.053588867
## 46  5.067772 -0.509619037
## 47  5.951270  0.938199770
## 48  5.773427 -0.925131977
## 49  6.327227  0.804502212
## 50  5.964545  0.884342418
## 51  4.605823  0.474921582
## 52  3.684059 -0.356924185
## 53  4.026997 -0.762949612
## 54  4.163418 -0.244593505
## 55  4.139998  0.428960012
## 56  5.018396 -0.256940627
## 57  3.516212 -1.623920314
## 58  5.141555 -1.337316404
## 59  5.960067  0.374642060
## 60  5.255333 -0.835293733
## 61  3.294183 -1.174187748
## 62  5.523669  2.369483915
## 63  4.423854  0.652292300
## 64  4.945777  1.057979355
## 65  3.936512 -0.600636670
## 66  3.709776 -0.415820328
## 67  4.900508  1.221371430
## 68  3.839682 -1.431215926
## 69  5.113746  1.048627001
## 70  3.593448 -0.626149168
## 71  3.615326 -1.290464542
## 72  5.079920  1.120011673
## 73  4.234177 -0.084026621
## 74  4.378034 -1.603083463
## 75  4.249208  0.196910551
## 76  4.761062  0.757726073
## 77  4.590446  0.024331940
## 78  5.185400  1.569498254
## 79  3.391183 -0.967208042
## 80  4.283544  0.011269304
## 81  5.306970  2.236807134
## 82  4.596331  0.243904982
## 83  4.213116 -0.448619630
## 84  5.200466 -1.118689505
## 85  5.332045 -0.940725057
## 86  5.021809 -1.879439748
## 87  5.537033 -0.413618119
## 88  5.531641  0.380748325
## 89  5.985854  0.069337688
## 90  3.519540 -1.578821610
## 91  3.432870 -0.847048706
## 92  5.080882 -0.344598910
## 93  5.023493 -0.252209940
## 94  4.222080  0.123004090
## 95  5.730839 -0.137050567
## 96  5.150712  0.933649813
## 97  5.140310 -0.497037255
## 98  5.588330 -0.490091918
## 99  5.918260 -0.232780841
## 100 5.678478 -0.006209866
#Smartphone Valence ICC
CCC(image_ratings_smartphone$valence_human, image_ratings_smartphone$valence_ml, 
    ci = "z-transform", conf.level = 0.95, na.rm = FALSE)
## $rho.c
##         est     lwr.ci    upr.ci
## 1 0.2496355 0.06248923 0.4198232
## 
## $s.shift
## [1] 0.7558816
## 
## $l.shift
## [1] 0.3420502
## 
## $C.b
## [1] 0.9108138
## 
## $blalt
##        mean       delta
## 1  5.010819  1.91921314
## 2  5.574274  0.95496401
## 3  5.831826 -0.24036190
## 4  5.134150 -0.60533612
## 5  5.307320 -1.36510305
## 6  5.808079 -0.88048266
## 7  5.806914 -0.84488180
## 8  3.757827 -3.44541445
## 9  5.714039 -0.51865151
## 10 5.478971  1.79621748
## 11 4.964480 -1.28940281
## 12 5.761132  1.96017546
## 13 4.941599  0.25013567
## 14 3.590095 -0.35796719
## 15 3.764079 -0.51152227
## 16 4.223812 -1.94854841
## 17 5.030330 -0.43843789
## 18 5.410856  0.48939836
## 19 6.232377  2.29080218
## 20 2.989497 -1.74195608
## 21 4.536822  1.74857781
## 22 4.127332  0.75274330
## 23 5.093996 -0.18428865
## 24 4.422665 -0.99690098
## 25 4.510587 -0.66561892
## 26 5.491943  1.12722606
## 27 4.444251  1.52554553
## 28 6.260963  1.37066660
## 29 2.902122 -2.75609593
## 30 4.285512 -2.00170746
## 31 6.122158 -0.97024226
## 32 5.574306 -1.17079387
## 33 4.781054 -0.91395993
## 34 5.285436 -1.24865029
## 35 5.699477 -1.34719750
## 36 4.150373 -0.74972640
## 37 4.967931 -1.26178696
## 38 3.826475  0.21742018
## 39 5.358619 -1.01668274
## 40 4.914971 -0.51142414
## 41 5.105919 -0.66706224
## 42 4.965517  0.21849936
## 43 4.583227  1.79623268
## 44 5.503869  0.43629192
## 45 5.782253  1.40745731
## 46 4.211114  1.04172842
## 47 5.713391 -1.66558799
## 48 3.668289 -2.95224882
## 49 6.281572 -1.42508415
## 50 4.939851 -0.38716564
## 51 2.708492 -2.05131168
## 52 5.624503  0.09427779
## 53 3.223345 -1.19295803
## 54 3.986705  0.41091848
## 55 4.901721 -0.07582983
## 56 3.350918 -0.79546798
## 57 5.040710 -0.59897954
## 58 4.025206 -1.17727724
## 59 5.332566 -0.15020731
## 60 4.344186  1.10640436
## 61 4.108419 -0.45191271
## 62 3.883077  0.27862266
## 63 5.640004 -0.09344142
## 64 5.474637  1.33162584
## 65 5.549609  1.43436504
## 66 5.284225  0.67781893
## 67 4.633546  1.91574441
## 68 4.952772 -0.77800321
## 69 6.120279 -0.40100658
## 70 3.179912 -3.39160025
## 71 5.712910  0.41373212
## 72 4.317287  1.25721682
## 73 4.909296 -1.98650322
## 74 5.495317 -0.57272278
## 75 6.039788 -1.84450219
## 76 5.024727 -1.61288602
## 77 5.487065 -1.03405455
## 78 6.071973 -1.25839227
## 79 4.183344  0.99375611
## 80 5.867570 -1.63066159
## 81 5.797201  1.02500019
## 82 5.754038 -0.19091091
## 83 5.536918  0.65004390
## 84 5.127951 -2.07306659
## 85 4.622624 -2.07733678
## 86 5.432084 -1.62536202
## 87 4.534335 -0.48284955
## 88 4.981384  0.45887290
#Smartphone Arousal ICC
CCC(image_ratings_smartphone$arousal_human, image_ratings_smartphone$arousal_ml, 
    ci = "z-transform", conf.level = 0.95, na.rm = FALSE)
## $rho.c
##         est    lwr.ci    upr.ci
## 1 0.4381846 0.2713722 0.5794399
## 
## $s.shift
## [1] 0.6794042
## 
## $l.shift
## [1] 0.1204585
## 
## $C.b
## [1] 0.9234496
## 
## $blalt
##        mean        delta
## 1  4.997177 -0.090649548
## 2  4.086234  0.334001750
## 3  4.656127 -1.371513330
## 4  5.894015  0.123080556
## 5  5.786730  0.215818956
## 6  5.539622 -0.401467111
## 7  5.413266 -0.737807708
## 8  6.038216  0.595181650
## 9  5.670027  0.452921985
## 10 5.274936 -0.894316222
## 11 3.325194 -1.075525816
## 12 5.258760 -0.007352821
## 13 4.129794  0.150764201
## 14 4.379955  0.249333072
## 15 4.703859  0.373764341
## 16 4.930257  1.566473230
## 17 4.215534 -0.660697815
## 18 4.266440 -0.365904359
## 19 4.890584  1.770684526
## 20 5.365189  1.595547963
## 21 3.898402 -0.820923369
## 22 3.483254 -1.492433263
## 23 3.334166 -1.139574121
## 24 4.790888  1.492298037
## 25 4.052831  0.164708185
## 26 4.426297  0.156683419
## 27 4.289259 -0.452591437
## 28 5.679035  0.201188930
## 29 5.382810  2.238084152
## 30 4.912491  0.989832007
## 31 4.832669 -1.698671667
## 32 5.775212  0.453279352
## 33 5.516847 -0.711471889
## 34 5.747799  0.237240795
## 35 3.305791 -1.130100959
## 36 5.677584 -0.390419232
## 37 5.634107 -0.601547367
## 38 5.572210  0.970394207
## 39 4.876308 -1.648719948
## 40 4.425896  0.053588867
## 41 3.684059 -0.356924185
## 42 4.026997 -0.762949612
## 43 4.163418 -0.244593505
## 44 4.139998  0.428960012
## 45 5.018396 -0.256940627
## 46 3.516212 -1.623920314
## 47 5.141555 -1.337316404
## 48 5.960067  0.374642060
## 49 5.255333 -0.835293733
## 50 3.294183 -1.174187748
## 51 5.523669  2.369483915
## 52 4.423854  0.652292300
## 53 4.945777  1.057979355
## 54 3.936512 -0.600636670
## 55 3.709776 -0.415820328
## 56 4.900508  1.221371430
## 57 3.839682 -1.431215926
## 58 5.113746  1.048627001
## 59 3.593448 -0.626149168
## 60 3.615326 -1.290464542
## 61 5.079920  1.120011673
## 62 4.234177 -0.084026621
## 63 4.378034 -1.603083463
## 64 4.249208  0.196910551
## 65 4.761062  0.757726073
## 66 4.590446  0.024331940
## 67 5.185400  1.569498254
## 68 3.391183 -0.967208042
## 69 4.283544  0.011269304
## 70 5.306970  2.236807134
## 71 4.596331  0.243904982
## 72 4.213116 -0.448619630
## 73 5.200466 -1.118689505
## 74 5.332045 -0.940725057
## 75 5.021809 -1.879439748
## 76 5.537033 -0.413618119
## 77 5.531641  0.380748325
## 78 5.985854  0.069337688
## 79 3.519540 -1.578821610
## 80 3.432870 -0.847048706
## 81 5.080882 -0.344598910
## 82 5.023493 -0.252209940
## 83 4.222080  0.123004090
## 84 5.730839 -0.137050567
## 85 5.150712  0.933649813
## 86 5.140310 -0.497037255
## 87 5.588330 -0.490091918
## 88 5.918260 -0.232780841
#OASIS Valence ICC
CCC(image_ratings_OASIS$valence_human, image_ratings_OASIS$valence_ml, 
    ci = "z-transform", conf.level = 0.95, na.rm = FALSE)
## $rho.c
##        est    lwr.ci    upr.ci
## 1 0.755242 0.5110502 0.8866293
## 
## $s.shift
## [1] 0.6272227
## 
## $l.shift
## [1] 0.4604461
## 
## $C.b
## [1] 0.8218401
## 
## $blalt
##        mean       delta
## 1  4.231094 -1.76348145
## 2  7.012616 -0.08866542
## 3  4.453889 -1.65084590
## 4  7.183542  0.07447522
## 5  5.989656 -0.56079414
## 6  5.247406 -1.14666423
## 7  2.956800 -1.90989615
## 8  4.701994  0.27077858
## 9  7.414296  1.23110875
## 10 2.954445 -1.64769608
## 11 3.434404 -0.77925568
## 12 4.779010 -0.33414040
#OASIS Arousal ICC
CCC(image_ratings_OASIS$arousal_human, image_ratings_OASIS$arousal_ml, 
    ci = "z-transform", conf.level = 0.95, na.rm = FALSE)
## $rho.c
##         est     lwr.ci    upr.ci
## 1 0.4134235 -0.1302138 0.7659374
## 
## $s.shift
## [1] 0.7188528
## 
## $l.shift
## [1] -0.20164
## 
## $C.b
## [1] 0.9299657
## 
## $blalt
##        mean        delta
## 1  5.084842  1.072459489
## 2  4.860327 -0.550560971
## 3  4.530509 -0.998170710
## 4  4.732736  1.203658619
## 5  4.417338 -0.634675400
## 6  5.067772 -0.509619037
## 7  5.951270  0.938199770
## 8  5.773427 -0.925131977
## 9  6.327227  0.804502212
## 10 5.964545  0.884342418
## 11 4.605823  0.474921582
## 12 5.678478 -0.006209866
#OASIS Human Valence ICC
CCC(image_ratings_OASIS$valence_human, image_ratings_OASIS$adjusted_valence, 
    ci = "z-transform", conf.level = 0.95, na.rm = FALSE)
## $rho.c
##         est    lwr.ci    upr.ci
## 1 0.9791759 0.9321853 0.9937115
## 
## $s.shift
## [1] 1.048807
## 
## $l.shift
## [1] 0.02386034
## 
## $C.b
## [1] 0.9985817
## 
## $blalt
##        mean        delta
## 1  3.488402 -0.278097930
## 2  6.977606 -0.018644522
## 3  3.817501 -0.378070144
## 4  7.371116 -0.300672924
## 5  6.150926 -0.883333333
## 6  4.620988  0.106172839
## 7  2.007099 -0.010493827
## 8  4.684124  0.306518981
## 9  8.039678 -0.019654205
## 10 2.083817  0.093559978
## 11 3.045490 -0.001428501
## 12 4.198127  0.827626574
#OASIS Human Arousal ICC
CCC(image_ratings_OASIS$arousal_human, image_ratings_OASIS$adjusted_arousal, 
    ci = "z-transform", conf.level = 0.95, na.rm = FALSE)
## $rho.c
##         est    lwr.ci   upr.ci
## 1 0.7856143 0.4656692 0.923927
## 
## $s.shift
## [1] 0.9158766
## 
## $l.shift
## [1] -0.4332529
## 
## $C.b
## [1] 0.9109813
## 
## $blalt
##        mean       delta
## 1  5.475553  0.29103909
## 2  4.554900  0.06029425
## 3  4.040464 -0.01808166
## 4  5.219063  0.23100575
## 5  3.998718  0.20256410
## 6  4.848789 -0.07165242
## 7  6.376852  0.08703704
## 8  4.899020  0.82368194
## 9  6.136583  1.18578829
## 10 6.049512  0.71440873
## 11 4.358535  0.96949717
## 12 5.786531 -0.22231664

Means and SDs

All Images
Raiting Mean SD
Arousal
Human 4.771561 1.0642441
Machine 4.846801 0.7234312
OASIS Adjusted 4.968157 0.8177350
Valence
Human 4.746738 1.3064268
Machine 5.142547 0.9511387
OASIS Adjusted 4.730261 2.0790387
By Images Source
Raiting Mean SD
Arousal
OASIS Human 5.322596 0.8928440
OASIS Machine 5.176453 0.6418234
Smartphone Human 4.696420 1.0679205
Smartphone Machine 4.801849 0.7255497
Valence
OASIS Human 4.683884 1.9822890
OASIS Machine 5.375974 1.2433367
Smartphone Human 4.755309 1.2019634
Smartphone Machine 5.110716 0.9085421

Day In the Life Deep Affect Module

Data Overview

glimpse(image_ratings_DITL)
## Rows: 1,151
## Columns: 6
## $ date_char  <dbl> 2.02108e+13, 2.02108e+13, 2.02108e+13, 2.02108e+13, 2.02108…
## $ name_image <chr> NA, NA, NA, NA, "DITL_va6", NA, NA, NA, NA, NA, NA, NA, NA,…
## $ date       <chr> "2021-08-12 21:28:11", "2021-08-12 21:28:16", "2021-08-12 2…
## $ valence    <dbl> 5.354269, 5.348342, 5.348478, 3.687168, 3.684448, 3.684448,…
## $ arousal    <dbl> 4.459608, 4.460746, 4.451868, 4.321830, 4.337230, 4.337230,…
## $ row_id     <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, …
kbl(describe(image_ratings_DITL[,c(2,3:4)])) %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = F)
vars n mean sd median trimmed mad min max range skew kurtosis se
name_image* 1 88 44.500000 25.5473417 44.50000 44.500000 32.6172000 1.000000 88.000000 87.000000 0.0000000 -1.240980 2.7233558
date* 2 1151 575.345786 332.0175040 575.00000 575.307275 425.5062000 1.000000 1150.000000 1149.000000 0.0012595 -1.202387 9.7864080
valence 3 1151 4.590755 0.7138819 4.43837 4.538283 0.8533066 3.665626 7.062879 3.397253 0.5017648 -0.824026 0.0210421

Plots

#By Order
image_ratings_DITL_long %>%
  ggplot(aes(x = row_id, y = value, col = rating)) +
  geom_point(aes(color = rating)) +
  geom_line(aes(color = rating)) +
  ggtitle("Deep Affect Module: By Order") +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5, face = "bold"))

#By Order with subset highlighted
image_ratings_DITL %>%
  ggplot(aes(x=row_id)) +
  geom_rect(xmin = 375, xmax = 1000, ymin = 0, ymax= 7, 
            fill = "grey90", alpha = 0.03) +
  geom_line(aes(y=valence), color = "blue", alpha = 0.5) +
  geom_line(aes(y=arousal), color = "red", alpha = 0.75) +
  ggtitle("Deep Affect Module: By Order") +
  scale_y_continuous(name = "Score") +
  scale_x_continuous(name = "Screenshot", n.breaks = 10) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5, face = "bold"),
        text = element_text(size = 16))